This unsorted list contains links/references to some documents citing a publication which I contributed to as an author or co-author.
  1. A. Fitzgibbon, A. Zisserman: Joint Manifold Distance: a new approach to appearance based clustering. In CVPR 2003.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  2. B Savas, L Elden: Handwritten Digit Classification using Higher Order Singular Value Decomposition. In Pattern Recognition, Vol. 40, No. 3, pp. 993-1003, March 2007.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  3. S Jafar, S Srinivasa: Capacity Limits of Cognitive Radio with Distributed and Dynamic Spectral Activity In: 2006 IEEE International Conference on Communications, June 2006, Volume 12, pages 5742-5747
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  4. S. Lazebnik, C. Schmid, and J. Ponce: A Maximum Entropy Framework for Combining Parts and Relations for Texture and Object Recognition in The Learning Workshop, Snowbird, Utah, April 5-8 2005.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  5. S. Lazebnik, C. Schmid, and J. Ponce: A Maximum Entropy Framework for Part-Based Texture and Object Recognition in Proceedings of the IEEE International Conference on Computer Vision, Beijing, China, October 2005
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  6. S. Lazebnik, C. Schmid, and J. Ponce: A Discriminative Framework for Texture and Object Recognition Using Local Image Features in Towards Category-Level Object Recognition, Springer Lecture Notes in Computer Science. C. Schmid, J. Ponce, M. Hebert, and A. Zisserman (eds.), to appear.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  7. R Maree, P Geurts, J Piater, and L Wehenkel: Random Subwindows for Robust Image Classification in CVPR 2005.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  8. Mey, DA; Sarholz, S; Terboven, C: Nested parallelization with OpenMP in International Journal of Parallel Programming 35 (5): 459-476 Oct 2007.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  9. Z. Kleber, S Guimares: Bipartite graph matching for video clip localization in 20th Brazilian Symposium on Computer Graphics and Image Processing, October 2007.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  10. M Lam, T Disney, M Pham, D Raicu, J Furst, R Susomboon: in SPIE Medical Imaging Conference, San Diego, CA, February 2007.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  11. X Zhu, AB Goldberg, M Eldawy, CR Dyer, B Strock: A Text-to-Picture Synthesis System for Augmenting Communication. AAAI 2007.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  12. D Iakovidis, N Pelekis, H Karanikas, E Kotsifakos, I Kopanakis, and Y Theodoridis: A Pattern Similarity Scheme for Medical Image Retrieval. In: Int. Special Topic Conference on Information Technology in Biomedicine, Ioannina - Epirus, Greece, October 26-28, 2006
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004) (and other papers).
  13. T Pham, A Smeulders: Learning spatial relations in object recognition. In Pattern Recognition Letters 27 (2006) 1673­1684
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  14. A. Opelt, A. Pinz, A. Zisserman: Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection. In Int J Comput Vis, 2008.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  15. A Teynor and H Burkhardt: Patch Based Localization of Visual Object Class Instances. In MVA2007 IAPR Conference on Machine Vision Applications, May 16-18, 2007, Tokyo, Japan
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  16. A Teynor, E Rahtu, L Setia, H Burkhardt: Properties of Patch Based Approaches for the Recognition of Visual Object Classes. In DAGM 2006, Pattern Recognition Symposium, Berlin, Germany.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  17. P Carbonetto, G Dorko, C Schmid, H Kück1, N de Freitas: Learning to recognize objects with little supervision. In IJCV (accepted)
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  18. L Setia, A Teynor, A Halawani and H Burkhardt: Image Classification using Cluster-Cooccurrence Matrices of Local Relational Features. MIR'06, pages 173-181, October 26­27, 2006, Santa Barbara, California, USA.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005) (and other papers).
  19. J Zhang, M Marszalek, S Lazebnik, C Schmid: Local Features and Kernels for Classification of Texture and Object Categories: An In-Depth Study. Technical Report RR-5737, INRIA Rhône-Alpes - Nov 2005.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005).
  20. J Zhang, M Marszalek, S Lazebnik, C Schmid: Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study. In Beyond Patches Workshop, in conjunction with CVPR - 2006. (similar document here, in IJCV, 2006)
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005).
  21. T Korah, C Rasmussen: PCA-based Recognition for Efficient Inpainting. In IEEE Asian Conference on Computer Vision, ACCV-06, Hyderabad, India, 2006.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005).
  22. J-H Lim, J-P Chevallet, S Gao: Scene Identification using Discriminative Patterns. In ICPR 2006, Vol. II, pp. 642-645, Hong Kong, China, August 2006
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005).
  23. Qi Li, Jieping Ye, Chandra Kambhamettu: Interest point detection using imbalance oriented selection. Pattern Recognition, v.41 n.2, p.672-688, February 2008.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005).
  24. N. Nakamura, S. Takano, and K. Niijima: Video Scene Retrieval Based on the Layerization of Images. in Image and Vision Computing New Zealand, University of Otago, Dunedin, 28 - 29 Nov, 2005.
    Cites FIRE (ImageCLEF 2004).
  25. Wei Xiong, Bo Qiu, Qi Tian, Changsheng Xu, S. H. Ong, Kelvin Foong, Jean-Pierre Chevallet: MultiPRE : A Novel Framework With Multiple Parallel Retrieval Engines For Content-Based Image Retrieval. In ACM Multimedia, Singapore, pp. 1023-1032, November 2005.
    Cites FIRE (ImageCLEF 2004).
  26. Z. Zhang, C. Rojas, O. Nasraoui, H Frigui: SHOW AND TELL: A Seamlessly Integrated Tool For Searching with Image Content And Text in Second NASA Data Mining Workshop, Pasadena, CA, May 2006.
    Cites FIRE in ImageCLEF 2005: Combining content-based image retrieval with textual information retrieval (ImageCLEF 2005).
  27. S v. Aschkenasy, C Jansen, R Osterwalder, A Linka, M Unser, s Marsch, and P Hunziker: Unsupervised image classification of medical ultrasound data by multiresolution elastic registration. In: Ultrasound in Med. & Biol., Vol. 32, No. 7, pp. 1047­1054, 2006
    Cites Determining the view of chest radiographs. (JDI 2003).
  28. H. Bekel, G. Heidemann, H. Ritter: Interactive Image Data Labeling Using Self-Organizing Maps in an Augmented Reality Scenario in Neural Networks, Vol. 18, No. 5/6, 2005.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  29. Abraham Bagherjeiran, Ricardo Vilalta, Christoph F. Eick: Content-Based Image Retrieval Through a Multi-Agent Meta-Learning Framework in 17thIEEE Int. Conf. on Tools with Artificial Intelligence, Hong Kong, China, November 2005.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  30. C Rother; V Kolmogorov; T Minka; A Blake: Cosegmentation of Image Pairs by Histogram Matching -- Incorporating a Global Constraint into MRFs in CVPR 2006, vol. 1, pp. 993-1000, New York, NY, June 2006.
    Cites Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems (ICPR 2004).
  31. F Perronnin, C Dance, G Csurka, and M Bressan: Adapted Vocabularies for Generic Visual Categorization in ECCV 2006, Part IV, LNCS 3954, pp. 464­475, 2006.
    Cites Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems (ICPR 2004).
  32. R. Ksantini, D. Ziou, B. Colin, and F. Dubeau: Logistic Regression Models for a Fast CBIR Method Based on Feature Selection. In IJCAI 2007, pages 2790-2795.
    Cites Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems (ICPR 2004).
  33. T. Coogan, A Sutherland: Transformation Invariance in Hand Shape Recognition. In ICPR 2006, Vol. III, pp. 485-488, Hong Kong, China, August 2006
    Cites the tangent distance implementation.
  34. Nadav Ben-Haim, Boris Babenko, Serge Belongie: Improving Web-based Image Search via Content Based Clustering. In: International Workshop on Semantic Learning Applications in Multimedia (SLAM) In association with CVPR 2006, New York, NY, June 2006.
    Cites Clustering visually similar images to improve image search engines (IT 2003).
  35. B. Patel and B. Meshram: Mining and Clustering Images to Improve Image Search Engines for Geo-Informatics Database. In: National Conference GEO Informatics ???.
    Cites Clustering visually similar images to improve image search engines (IT 2003).
  36. R. Ksantini, D. Ziou, B. Colin, F. Dubeau: Weighted Pseudo-Metric Discriminatory Power Improvement Using a Bayesian Logistic Regression Model Based on a Variational Method Rapport technique 16, DI, Université de Sherbrooke, Sherbrooke, QC, Canada, 30 pages, 2006.
    Cites Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems (ICPR 2004).
  37. M. Springmann, H. Schuldt: Speeding up IDM without Degradation of Retrieval Quality. In Working Notes for the CLEF 2007 Workshop, 19-21 September, Budapest, Hungary.
    Cites Deformation Models for Image Recognition (PAMI 2007) (and other papers).
  38. M. Springmann, A. Dander, H. Schuldt: Improving efficiency and effectiveness of the image distortion model. In Pattern Recognition Letters, Volume 29, Issue 15 (November 2008) Pages 2018-2024.
    Cites Deformation Models for Image Recognition (PAMI 2007) (and other papers?).
  39. M Hein and M Maier: Manifold Denoising. In Advances in Neural Information Processing Systems 20, 8, MIT Press, Cambridge, MA, 2006.
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  40. M Hein and M Maier: Manifold Denoising As Preprocessing for Finding Natural Representations of Data. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07), 1646-1649.
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  41. Liu CL, Sako H: Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition. In PATTERN RECOGNITION 39(4):669-681 April 2006
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  42. Cheng, PC; Chien, BC; Ke, HR; Yang, WP: Combining textual and visual features for cross-language medical image retrieval. In: Accessing multilingual information repositories, Springer LNCS 4022: 712-723, 2006
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  43. Bouguila, N; Ziou, D; A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized dirichlet mixture. In IEEE Transactions on Image Processing 15 (9): 2657-2668 Sep 2006.
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  44. Xiuwen Liu, Washington Mio: Splitting factor analysis and multi-class boosting. In ICIP 2006, pages 949-952.
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  45. X Tan, S Chen, Z-H Zhou, F Zhang: Face Recognition from a Single Image per Person: A Survey in ???.
    Cites Enhancements for Local Feature Based Image Classification (ICPR 2004).
  46. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Laiwan Chan: The Minimum Error Minimax Probability Machine. In Journal of Machine Learning Research 5 (2004) 1253-1286.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  47. G. Loosli, S. Canu, S.V.N. Vishwanathan , A.J. Smola: Invariances in Classification: an efficient SVM implementation in ASMDA 2005, International Symposium on Applied Stochastic Models and Data Analysis.
    Cites Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition (SPR 2002).
  48. L. Wang, Y. Zhang, J. Feng: On the Euclidean Distance of Images in IEEE TPAMI, Volume 27, Issue 8, pages 1334-1339, August 2005.
    Cites Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition (SPR 2002).
  49. J. Cano and J.-C. Perez-Cortes: Vehicle License Plate Segmentation In Natural Images in IbPRIA 2003.
    Cites Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition (SPR 2002).
  50. Jianguo Lee, Jingdong Wang, Changshui Zhang, and Zhaoqi Bian: Visual Object Recognition using Probabilistic Kernel Subspace Similarity. Pattern Recognition 38:997-1008, 2005
    Cites Learning of Variability for Invariant Statistical Pattern Recognition (ECML2001)
  51. P Dollar, V Rabaud and S Belongie: Learning to Traverse Image Manifolds. In: NIPS 2006.
    Cites Learning of Variability for Invariant Statistical Pattern Recognition (ECML2001)
  52. P Dollar, V Rabaud and S Belongie: Non-Isometric Manifold Learning: Analysis and an Algorithm. In: Proceedings of the 24th international conference on Machine learning, Corvalis, Oregaon, 2007.
    Cites Learning of Variability for Invariant Statistical Pattern Recognition (ECML2001)
  53. Peschke, K.-D., Haasdonk, B., Ronneberger, O., Burkhardt, H., Rösch, P., Harz, M. and Popp, J.: Using Transformation Knowledge for the Classification of Raman Spectra of Biological Samples. BioMed 2006, Proc. of the 4th IASTED International Conference on Biomedical Engineering, pp. 288-293, 2006
    Cites Learning of Variability for Invariant Statistical Pattern Recognition (ECML2001)
  54. Zhaoqi Bian, Jianguo Lee, Jingdong Wang, Changshui Zhang.: Probabilistic Tangent Subspace: A Unified View in ICML 2004. .ps
    Cites Learning of Variability for Invariant Statistical Pattern Recognition (ECML2001)
  55. C.-S. Chen: Fast Algorithm for Robust Template Matching With M-Estimators in IEEE TSP 51(1):230-243, Jan. 2003.
    Cites Learning of Variability for Invariant Statistical Pattern Recognition. (ECML 2001).
  56. B. Haasdonk, A. Halawani, H. Burkhardt: Adjustable Invariant Features by Partial Haar-Integration in ICPR 2004, International Conference on Pattern Recognition, Cambridge, UK, Volume II, pages 769-774, August 2004.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  57. Mendes, A., Bento, L.C., Nunes, U.: Multi-target detection and tracking with a laser scanner in 2004 IEEE Intelligent Vehicles Symposium, pages 796-801, June 2004.
    Cites Local Representations for Multi-Object Recognition. (DAGM 2003).
  58. M Lecca and S Messelodi: Recognition and Reconstruction of Partially Occluded Objects In Transactions on Engineering, Computing and Technology volume 16 November 2006.
    Cites Local Representations for Multi-Object Recognition. (DAGM 2003).
  59. Mendes, A., Nunes, U.: Situation-based Multi-target Detection and Tracking with Laserscanner in Outdoor Semi-structured Environment in Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems September 28 - October 2, 2004, Sendai, Japan.
    Cites Local Representations for Multi-Object Recognition. (DAGM 2003).
  60. R. Fergus, P. Perona, A. Zisserman: A Visual Category Filter for Google Images in ECCV 2004.
    Cites Clustering visually similar images to improve image search engines (IT 2003).
  61. S. Zinger, C. Millet, B. Mathieu, G. Grefenstette, P. Hède, P.-A. Moëllic: Clustering and semantically filtering web images to create a large-scale image ontology in Proceedings of the IS&T/SPIE 18th Symposium Electronic Imaging 2006, January 15-19, 2006, San Jose, CA.
    Cites Clustering visually similar images to improve image search engines (IT 2003).
  62. S. Zinger, C. Millet, B. Mathieu, G. Grefenstette, P. Hede, P.-A. Moellic: Extracting an Ontology of Portrayable Objects from WordNet. In Proceedings of the MUSCLE / ImageCLEF Workshop on Image and Video Retrieval Evaluation, Vienna, Austria, September 20, 2005.
    Cites Clustering visually similar images to improve image search engines (IT 2003).
  63. A.D. Parkins, A.K. Nandi: Method for calculating first-order derivative based feature saliency information in a trained neural network and its application to handwritten digit recognition . In IEE Proceedings-Vision Image And Signal Processing 152 (2): 137-147 Apr 2005.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  64. A. D. Parkins, A. K. Nandi: Simplifying Hand Written Digit Recognition Using A Genetic Algorithm in EUSIPCO 2002 (?).
    Cites Experiments with an extended tangent distance (ICPR 2000).
  65. Hansheng Lei, Venu Govindaraju: Direct Image Matching by DynamicWarping in IEEE Workshop on Face Processing in Video 2004.
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  66. Tao Ju, Joe Warren, James Carson, Musodiq Bello, Ioannis Kakadiaris, Wah Chiu, Christina Thaller, Gregor Eichele: 3D volume reconstruction of a mouse brain from histological sections using warp filtering in Journal of Neuroscience Methods, 2006
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  67. Liskiewicz M, Wolfel U: On the intractability of inverting geometric distortions in watermarking schemes. In Information Hiding 3727: 176-188, 2005
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  68. Keith Rennolls and Mingliang Wang: Enhancement of image-to-image co-registration accuracy using spectral matching methods. In 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pages 296-305.
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  69. Hayit Greenspan and Adi T. Pinhas: Medical Image Categorization and Retrieval For PACS Using the GMM-KL Framework. In IEEE Transactions on Information Technology in BioMedicine, 11 (2): 190-202 MAR 2007
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  70. Mehran Moshfeghi, Craig Saiz, and Hua Yu: Content-based Retrieval of Medical Images with Relative Entropy. In: Medical Imaging 2004: PACS and Imaging Informatics. Proceedings of the SPIE, Volume 5371, pp. 259-267 (2004).
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  71. Siadat, MR; Soltanian-Zadeh, H; Fotouhi, F; Elisevich, K: Content-based image database system for epilepsy. Computer methods and programs in biomedicine, 79 (3): 209-226 Sep 2005.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  72. D Tahmoush and H Samet: A Web Collaboration System for Content-Based Image Retrieval of Medical Images. In: Proceedings of SPIE Medical Imaging 2007 - PACS and Imaging Informatics- Vol.6516 - San Diego, CA, Feb 2007
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  73. T Berlage: Analyzing and mining image databases. In Drug Discovery Today, 10 (11): 795-802 Jun 1 2005.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  74. D.V. Tsishkou: Boosting Biomedical Images Indexing, in IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  75. M Liwicki et al.: Open Source, Open Content, Social Aspects and Future of DAS.
    Cites Integrated Handwriting Recognition and Interpretation using Finite State Models (IJPRAI 2004).
  76. Y Kim and S Ross: Genre Classification in Automated Ingest and Appraisal Metadata. In: European Conference On Research And Advanced Technology For Digital Libraries (ECDL) LNCS 4172, pages pp. 63-74, Alicante, Spain.
    Cites Performance Comparison of Six Algorithms for Page Segmentation (DAS 2006).
  77. Y Kim and S Ross: "The Naming of Cats": Automated Genre Classification. In: 2nd International Digital Curation Conference, Digital Data Curation in Practice, 21-22 November 2006, Glasgow
    Cites Performance Comparison of Six Algorithms for Page Segmentation (DAS 2006).
  78. H Cao, R Prasad, P Natarajan, E MacRostie: Robust Page Segmentation Based on Smearing and Error Correction Unifying Top-down and Bottom-up Approaches. In: ICDAR 2007, Curitiba, Brazil, September 2007.
    Cites Performance Comparison of Six Algorithms for Page Segmentation (DAS 2006).
  79. G. Olague, F. Fernandez, C. Pérez, E. Lutton: The Infection Algorithm: an Artificial Epidemic Approach for Dense Stereo Matching in VIII Parallel Problem Solving from Nature Conference. LNCS 3242. pp. 622-632.
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  80. G. Olague, F. Fernandez, C. Pérez, E. Lutton: The Infection Algorithm: an Artificial Epidemic Approach for Dense Stereo Correspondence in ARTIFICIAL LIFE 12 (4): 593-615 FAL 2006
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  81. Peter Howarth, Alexei Yavlinsky, Daniel Heesch, and S. Rüger: Medical Image Retrieval Using Texture, Locality and Colour in ImageCLEF 2004 (to appear as Springer LNCS).
    Cites FIRE @ ImageCLEF 2004.
  82. P. Vincent, Y. Bengio: Manifold Parzen Windows, .ps in NIPS-15 2002/3
    Cites A Probabilistic View on Tangent Distance. (DAGM 2000) and Structured Covariance Matrices for Statistical Image Object Recognition. (DAGM2002).
  83. S.A. Karkanis et al.: Computer-Aided Tumor Detection in Endoscopic Video Using Color Wavelet Features in IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 3, September 2003.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  84. R. Mariani: A face location and recognition system based on tangent distance in Multimodal interface for human-machine communication, World Scientific Publishers, April 2002.
    Cites Experiments with an extended tangent distance (ICPR 2000) and Invariant Image Object Recognition using Mixture Densities (ICPR 2000).
  85. M. Reinhold, D. Paulus, H. Niemann: Appearance-Based Statistical Object Recognition by Heterogeneous Background and Occlusion, in Proc. Pattern Recognition (DAGM) 2001, (Springer LNCS 2191) pp. 254-261.
    Cites An Automatic Approach to Invariant Radiograph Classification (BVM 2001),
  86. M. Grzegorzek, K. N. Pasumarthy, M. Reinhold, H. Niemann: Statistical Object Recognition for Multi-Object Scenes with Heterogeneous Background, in 4th Indian Conference on Computer Vision, Graphics and Image Processing. Kolkata : Allied Publishers Private Limited, 2004, pp. 222-227.
    Cites Local Representations for Multi-Object Recognition. (DAGM 2003).
  87. P. Geurts: Contributions to Decision Tree Induction: Bias/Variance Tradeoff and Time Series Classification, HTML, PhD thesis, University of Liege, Belgium, 2002.
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001).
  88. S Jodogne: Closed-loop learning of visual control policies. PhD thesis, University of Liege, Belgium, 2007.
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  89. P Howarth: Discovering images: features, similarities and subspaces. PhD Thesis, Imperial College London, 2007
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  90. T.K. Kämpfe: . In: PhD thesis, University of Bieleifeld, May 2006.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  91. : Partitioning of the Feature Set for Classifier Cooperations. In: Journal of Engineering Creation and Technology, 2006, ISSN 1409-5564
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001).
  92. B. Haasdonk: Transformation Knowledge in Pattern Analysis with Kernel Methods. PhD thesis, Computer Science Department, University of Freiburg, May 2005.
    Cites Local Context in Non-linear Deformation Models for Handwritten Character Recognition (ICPR 2004) (and others).
  93. H. Yu, M. Li, H.-J. Zhang, J. Feng: Color texture moments for content-based image retrieval in ICIP 2002 (?)
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  94. Cheng-Lin Liu, Hiromichi Fujisawa: Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems in Neural Networks and Learning in Document Analysis and Recognition, First IAPR TC3 NNLDAR Workshop, Seoul, Korea, August 29, 2005, pages 1-7.
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001).
  95. R. Muniz, J.A. Corrales: Use of Band Ratioing for Color Texture Classification, in IbPRIA 2003, LNCS 2652.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  96. R. Muniz, J.A. Corrales: Novel Techniques for Color Texture Classification. In IPCV 2006, pages 114-120.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  97. D. Cakmakov, V. Radevski, Y. Bennani, D. Gorgevik: Decision Fusion and Reliability Control in Handwritten Digit Recognition System in Journal of Computing and Information Technology - CIT 10, 2002, 4, pp. 283-293.
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001).
  98. D. Gorgevik, D. Cakmakov: Cooperation of support vector machines for handwritten digit recognition trough partitioning of the feature set in ETAI 2003 pp. I102-I108, Ohrid, Republic of MACEDONIA, 17 September 2003.
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001).
  99. S. Ghebreab, C.C. Jaffe, A.W.M. Smeulders: Population-based incremental interactive concept learning for image retrieval by stochastic string segmentations in IEEE Transactions on Medical Imaging, 23(6):676-689, 2004.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  100. S. Ghebreab, C.C. Jaffe, A.W.M. Smeulders: Concept-Based Retrieval of Biomedical Images in SPIE Medical Imaging 2003.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  101. H. Müller, P. Ruch, A. Geissbuhler: Enriching content-based medical image retrieval with automatically extracted MeSH-terms in GMDS 2004 (abstract).
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  102. H. Müller, A. Rosset, J.-P. Vallée, A. Geissbuhler: Integrating Content-Based Visual Access Methods into a Medical Case Database in MRV2003.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  103. H. Müller, A. Rosset, J.-P. Vallée, A. Geissbuhler: Comparing feature sets for content based image retrieval in a medical case database in SPIE 2004.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  104. M-R Siadat, H Soltanian-Zadeh, F Fotouhi, K Elisevich: in Computer Methods and Programs in Biomedicine (2005) 79, 209-226.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  105. H. Müller, N. Michoux, D. Bandon and A. Geissbuhler : A Review of Content-Based Image Retrieval Systems in Medical Applications -- Clinical Benefits and Future Directions in International Journal of Medical Informatics, Vol. 73, pp. 1-23, 2004.
    Cites e.g. Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  106. H. Müller, A. Rosset, J.-P. Vallée, F. Terrier, A. Geissbuhler: A reference data set for the evaluation of medical image retrieval systems in Computerized Medical Imaging and Graphics 2004.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003).
  107. H. Müller, J. Heuberger, A. Geissbuhler: Logo and text removal for medical image retrieval. In BVM 2005, Workshop Bildverarbeitung für die Medizin.
    Cites Content-based image retrieval in medical applications. (MIM 2005)
  108. Lim JH, Chevallet JP: VisMed: A visual vocabulary approach for medical image indexing and retrieval. In INFORMATION RETRIEVAL TECHNOLOGY, Second Asia Information Retrieval Symposium, AIRS 2005, Jeju Island, Korea, October 13-15, LNCS 3689: 84-96 2005
    Cites Content-based image retrieval in medical applications. (MIM 2005)
  109. M Costa Oliveira, W Cirne, PM de Azevedo Marques: Towards applying content-based image retrieval in the clinical routine. Future Generation Computer Systems, Vol. 23, No. 3, pp. 466-474, March 2007.
    Cites Content-based image retrieval in medical applications. (MIM 2005)
  110. C Thies, MO Güld, B Fischer, TM Lehmann: Content-based queries on the CasImage database with the IRMA framework: A field report. In CLEF 2004 Workshop. Bath, UK, pages 611-620, September 2004.
    Cites Classification of Medical Images using Non-linear Distortion Models (BVM 2004).
  111. MO Güld, C Thies, B Fischer, TM Lehmann: Combining global features for content-based retrieval of medical images. In CLEF 2005 Workshop, Vienna, Austria, 21-23 September 2005.
    Cites Classification of Medical Images using Non-linear Distortion Models (BVM 2004).
  112. Filip I. Florea, Alexandrina Rogozan, Abdelaziz Bensrhair, Stefan J. Darmoni: Comparison of Feature-Selection and Classification Techniques for Medical Images Modality Categorization. Technical Report INSA Rouen, France.
    Cites "Comparison of global features for categorization of medical images, Proceedings SPIE 2004" and "The irma pro ject - a state of the art report on content-based image retrieval in medical applications, Proceedings 7th Korea-Germany Joint Workshop on AdvancedMedical Image Processing 2003."
  113. I. Vanhamel, A. Katartzis, H. Sahli: Hierarchical segmentation via a diffusion scheme in color/texture feature space in ICIP 2003
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  114. S. Uchida, H. Sakoe: A preliminary study of pixel-based motion compensation in ISEE 2003.
    Cites Elastic Image Matching is NP-complete (PRL 2003).
  115. S. Uchida, H. Sakoe: A survey of elastic matching techniques for handwritten character recognition. In: IEICE Transactions on information and systems, E88D (8): 1781-1790, August 2005.
    Cites Elastic Image Matching is NP-complete (PRL 2003) and Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004) and Experiments with an extended tangent distance (ICPR 2000) and Local Context in Non-linear Deformation Models for Handwritten Character Recognition (ICPR 2004).
  116. L. Berthouze, A. Tijsseling: Acquiring ontological categories through interaction. In The Journal of Three Dimensional Images 16(4), pp. 141-147, 2002.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  117. R Maree, P Geurts, J Piater, and L Wehenkel: A GENERIC APPROACH FOR IMAGE CLASSIFICATION BASED ON DECISION TREE ENSEMBLES AND LOCAL SUB-WINDOWS in ACCV 2004 (Sixth Asian Conference on Computer Vision).
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method'. (MCS 2001).
  118. R Maree, P Geurts, G Visimberga, J Piater, and L Wehenkel: A Comparison of Generic Machine Learning Algorithms for Image Classification in "Research and Development in Intelligent Systems XX".
    Cites Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method'. (MCS 2001).
  119. Jingdong Wang, Jianguo Lee, Changshui Zhang: Kernel Trick Embedded Gaussian Mixture Model in The 14th International Conference on Algorithmic Learning Theory(ALT2003), Hokkaido University, Sapporo, Japan, Oct., 2003 Lecture Notes in Artificial Intelligence Vol. 2842, 2003, 159 - 174
    Cites Statistical Image Object Recognition using Mixture Densities. (JMIV 2001).
  120. Reinhold MP, Grzegorzek M, Denzler J, Niemann H: Appearance-based recognition of 3-D objects by cluttered background and occlusions. In Pattern Recognition 38(5):739-753 May 2005
    Cites Statistical Image Object Recognition using Mixture Densities. (JMIV 2001).
  121. Ognjen Arandjelovic, Roberto Cipolla: Incremental Learning of Temporally-Coherent Gaussian Mixture Models, in BMVC 2005.
    Cites Statistical Image Object Recognition using Mixture Densities. (JMIV 2001).
  122. Ioan Ispas , Eduard Franti , Florin Lazo: The complexity of the algorithms for the image recognition and classification, Proceedings of the WSEAS International Conference on Applied Computing Conference, p.160-164, May 27-30, 2008, Istanbul, Turkey
    Cites Statistical Image Object Recognition using Mixture Densities. (JMIV 2001).
  123. Ioan Ispas , Eduard Franti , Sanda Osiceanu, Marius Stoian: Basic elements in the modelling of the problem of the image recognition and classification, Proceedings of the 6th WSEAS International Conference on Applied Electromagnetics, Wireless and Optical, p.135-139, July 02-04, 2008, Trondheim, Norway
    Cites Statistical Image Object Recognition using Mixture Densities. (JMIV 2001).
  124. H.-T. Lin, C.-J. Lin: A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, 2003.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  125. D. Schneegaß, S. Udluft, T. Martinetz: Kernel rewards regression: an information efficient batch policy iteration approach In 24th IASTED Int Multi-Conference Artificial Intelligence and Applications, February 2006, Innsbruck, Austria.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  126. D. Zhang, X. Chen, W.S. Lee: Text Classification with Kernels on the Multinomial Manifold in SIGIR 2005, Salvador, Brazil.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  127. B.J. Jain, P. Geibel, F. Wysotzki: SVM Learning with the Schur-Hadamard Inner Product for Graphs in Proceedings of the 12th European Symposium on Artificial Neural Networks, ESANN'04.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  128. B.J. Jain, P. Geibel, F. Wysotzki: Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. In: KI 2004 Advances In Artificial Intelligence: 27th Annual German Conference In AI, KI 2004, pp. 241-255, Ulm, Germany, September 2004. (LNCS 3238) (A talk with the same title has apparently taken place here.)
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  129. B.J. Jain, P. Geibel, F. Wysotzki: Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. In: Neurocomputing, vol. 64, pp. 63-105, 2005
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  130. V Guigue, A Rakotomamonjy, S Canu: Translation invariant classification of non-stationary signals in 13th European Symposium on Artificial Neural Networks, Bruges, 2005.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  131. H Zhang, W Huang, Z Huang, B Zhang: A Kernel Autoassociator Approach to Pattern Classification in IEEE Trans Systems, Man and Cybernetics, Part B, Vol.35, Iss.3, June 2005, Pages 593-606.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  132. A. Pozdnoukhov and S. Bengio: Tangent Vector Kernels for Invariant Image Classification with SVMs In Proc. ICPR, Cambridge, UK, August 2004, Volume III, pages 486-489.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  133. A. Pozdnoukhov and S. Bengio: Tangent Vector Kernels for Invariant Image Classification with SVMs Technical Report, IDIAP, Martigny, Switzerland.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  134. A Teynor: Patch Based Approaches for the Recognition of Visual Object Classes - A Survey. In: Technical Report, Albert-Ludwigs-Univ. Freiburg, Institut Fur Informatik, Lehrstuhl für Mustererkennung und Bildverarbeitung, November 2006.
    Cites Modelling of Image Variability for Recognition (PhD Thesis 2006).
  135. G. Wu, E.Y. Chang, and Z. Zhang: An Analysis of Transformation on Non-Positive Semidefinite Similarity Matrix for Kernel Machines. International Conference on Machine Learning (ICML), Bonn Germany, August 2005.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  136. F Lauer, G Bloch: Incorporating Prior Knowledge in Support Vector Machines for Classification: a Review. In Neurocomputing 71, 7-9 (2008) 1578-1594.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  137. Guan Conghui, Xuan Guorong: Tangent Mahalanobis Distance and Its application to Medical Image Analysis in International Conference on Diagnostic Imaging and Analysis, pp. 131-136, ICDIA'02, August 18-20, 2002, Shanghai, China.
    Cites Classification of Radiographs in the `Image Retrieval in Medical Applications' System (IRMA) (RIAO 2000).
  138. Tan Rui, Xuan Guorong: A Classification Method of Diagnostic Image based on Tangent Distance. International Conference on Diagnostic Imaging and Analysis, pp. 157-161, ICDIA'02, August 18-20, 2002, Shanghai, China.
    Cites Classification of Radiographs in the `Image Retrieval in Medical Applications' System (IRMA) (RIAO 2000).
  139. Jaume Amores, Petia Radeva: Elastic Matching and Retrieval of IVUS Images Using Contextual Information in CCIA 2003, pp.137-148.
    Cites Classification of Radiographs in the `Image Retrieval in Medical Applications' System (IRMA) (RIAO 2000).
  140. Jaume Amores, Petia Radeva: Registration and Retrieval of Highly Elastic Bodies using Contextual Information in Pattern Recognition Letters 26 (2005) 1720­1731.
    Cites Classification of Radiographs in the `Image Retrieval in Medical Applications' System (IRMA) (RIAO 2000).
  141. Jaume Amores, Petia Radeva: Medical Image Retrieval Based on Plaque Appearance, Chapter in "Plaque Imaging Book", IFMBE press, ed. J. Suri et.al. (to appear)
    Cites Classification of Radiographs in the `Image Retrieval in Medical Applications' System (IRMA) (RIAO 2000).
  142. A. J. M. Traina, N. A. Rosa, Caetano Traina Jr.: Integrating Images to Patient Electronic Medical Records through Content-based Retrieval Techniques in 16th IEEE Symposium on Computer-based Medical Systems (CBMS2003), New York, June 26-27, 2003, pp. 163-168.
    Cites IRMA@VISIM2001.
  143. S. Antani, D.J. Leeb, L.R. Longa, G.R. Thoma: Evaluation of shape similarity measurement methods for spine X-ray images in J. Vis. Commun. Image R. 15 (2004) 285 302.
    Cites IRMA for PACS (SPIE 2003).
  144. S. Antani, L. Rodney Long, George R. Thoma, D.J. Lee: Anatomical Shape Representation in Spine X-ray Images in VIIP 2003.
    Cites IRMA for PACS (SPIE 2003).
  145. R Maree, P Geurts, and L Wehenkel: Random subwindows and extremely randomized trees for image classification in cell biology. In: BMC Cell Biology supplement on Workshop of Multiscale Biological Imaging, Data Mining and Informatics, 2007 Cites Enhancements for Local Feature Based Image Classification (ICPR 2004) and Automatic Classification of Red Blood Cells using Gaussian Mixture Densities (BVM 2000) and Combined Classification of Handwritten Digits using the 'Virtual Test Sample Method' (MCS 2001)
  146. H. Niemann: Klassifikation von Mustern (in German). (2. überarbeitete Auflage im Internet)
    Cites Experiments with an extended tangent distance (ICPR 2000) and Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004) in Chapter 3 as well as Automatic Classification of Red Blood Cells using Gaussian Mixture Densities (BVM 2000) in Chapter 4
  147. A. Bayer: Klassifikationsverfahren zur Materialerkennung - Grenzschichterkennung mittels laserinduzierter Fluoreszenz in mineralischen Lagerstätten am Beispiel der Braunkohlegewinnung (in German), Dissertation, RWTH Aachen, 2005.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002) and Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  148. M. Skosan, D. Mashao: Improving Speaker Identification Performance for Telephone-based Applications in SATNAC2004, pages 135-140.
    Cites Matching Training and Test Data Distributions for Robust Speech Recognition (Speech Comm. 2003).
  149. LIU Bo, DAI Li-Rong, WANG Ren-Hua, DU Jun, LI Jin-Yu: Double Gaussian GMM Based Feature Normalization and Its Application in Speech Recognition. In Acta Automatica Sinica, Vol. 32, No. 4, July 2006.
    Cites Matching Training and Test Data Distributions for Robust Speech Recognition (Speech Comm. 2003).
  150. Yoonjae Lee, Hanseok Ko: A New Feature Normalization Scheme Based on Eigenspace for Noisy Speech Recognition. In String Processing and Information Retrieval: 11th International Conference, SPIRE 2004, Padova, Italy, October 5-8, 2004, LNCS 3246, pp.76-78.
    Cites Matching Training and Test Data Distributions for Robust Speech Recognition (SpeechCom 2003).
  151. Yoonjae Lee, Hanseok Ko: Multi-Eigenspace Normalization for Robust Speech Recognition in Noisy Environments. In INTERSPEECH 2004 - ICSLP 8th International Conference on Spoken Language Processing, pp. 2097-2100, Jeju Island, Korea, October 2004.
    Cites Matching Training and Test Data Distributions for Robust Speech Recognition (SpeechCom 2003).
  152. M Skosan, D Mashao: Modified Segmental Histogram Equalization for robust speaker verification. In Pattern Recognition Letters, Volume 27, Issue 5 (April 2006), Pages 479-486.
    Cites Matching Training and Test Data Distributions for Robust Speech Recognition (SpeechCom 2003).
  153. L. Soualmia, A Neveol, M. Douyere et al.: Une Terminologie du Domaine Medical: Structure et Exploitation (in French) in Workshop on Terminology, Ontolgy and Knowledge Representation, January 2004.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  154. Qiang Wang, V Megalooikonomou, D Kontos: A Medical Image Retrieval Framework in 2005 IEEE Workshop on Machine Learning for Signal Processing, Sep. 2005.
    Cites Statistical Framework for Model-based Image Retrieval in Medical Applications (JEI 2003)
  155. Bo Qiu, Daniel Racoceanu, Chang Sheng Xu, and Qi Tian: Stripe: Image Feature Based on a New Grid Method and Its Application in ImageCLEF. In: Asia Information Retrieval Symposium, LNCS 4182, pp. 489­496, 2006.
    Cites Improving a Discriminative Approach to Object Recognition using Image Patches (DAGM 2005) and Cites FIRE in ImageCLEF 2005: Combining content-based image retrieval with textual information retrieval (ImageCLEF 2005).
  156. M. Wimmer, B. Radig, M. Beetz: Adaptive Skin Color Classifier for Face Outline Models in GVIP 05 Conference, 19-21 December 2005, CICC, Cairo, Egypt.
    Cites Appearance-Based Recognition of Words in American Sign Language (IbPRIA 2005).
  157. Yuan Niu, Yi-Min Wang, Hao Chen, Ming Ma, and Francis Hsu: A Quantitative Study of Forum Spamming Using Context-based Analysis. In: Proceedings of the 14th Annual Network and Distributed System Security Symposium (NDSS), San Diego, CA, February, 2007.
    Cites Round-Trip HTML Rendering and Analysis for Testing, Indexing, and Security (DAS 2006).
  158. M. Cuturi: Learning from Structured Objects with Semigroup Kernels. Thesis, November 2005.
    Cites Tangent Distance Kernels for Support Vector Machines (ICPR 2002).
  159. Socrates Dimitriadis, Kostas Marias, Stelios C. Orphanoudakis: A Versatile Image Retrieval Platform based on a Multi-agent Architecture. In: 6th International Conference on Visual Information Systems, pp. 387-392, September 24-26, Florida, USA, 2003
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  160. J. Kaminski, C. Rühl, F. Wolfsgruber, and G. Häusler: Robust Detection of Psoriatic Lesions in Annual Report of the Chair for Optics, Univ. Erlangen-Nuremberg, 2004.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  161. Max van Kleek: Providing proactive support for task and interrupt management. PhD Thesis proposal at MIT CSAIL.
    Cites Linear discriminant analysis and discriminative log-linear modeling (ICPR 2004).
  162. Lena Gorelick, Meirav Galun, Eitan Sharon2 Ronen Basri, Achi Brandt: Shape Representation and Classification Using the Poisson Equation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, December 2006
    Cites Local Context in Non-linear Deformation Models for Handwritten Character Recognition (ICPR 2004).
  163. SHAN Shi-Guang, GAO Wen, CHANG Yi-Zheng, CAO Bo, CHEN Xi-Lin: Curse of Mis-alignment Problem in Face Recognition. In Chinese Journal of Computers, vol. 28 No. 5, May 2005
    Cites Adaptation in Statistical Pattern Recognition Using Tangent Vectors (PAMI 2004).
  164. T. Nakai, K. Kise, M. Iwamura: Camera Based Document Image Retrieval with More Time and Memory Efficient LLAH. In CBDAR 2007, 2nd International Workshop on Camera-Based Document Analysis and Recognition, pp. 21-28.
    Cites Oblivious Document Capture and Real-Time Retrieval (CBDAR 2005).
  165. M. Iwamura, R. Niwa, K. Kise, S. Uchida, S. Omachi: Rectifying Perspective Distortion into Affine Distortion Using Variants and Invariants. In CBDAR 2007, 2nd International Workshop on Camera-Based Document Analysis and Recognition, pp. 138-144.
    Cites Oblivious Document Capture and Real-Time Retrieval (CBDAR 2005).
  166. C. Lampert, M. Blaschko, T. Hofmann: Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In CVPR 2008 (best paper award).
    Cites Optimal Geometric Matching for Patch-Based Object Detection (ELCVIA 2007).
  167. C. Lampert, M. Blaschko, T. Hofmann: A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection. In DAGM 2008.
    Cites Optimal Geometric Matching for Patch-Based Object Detection (ELCVIA 2007).
  168. Huang, Kaizhu: Learning From Data Locally and Globally PhD Thesis, Chinese Univ. of Hong Kong, July 2004.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  169. Liu WX, Zheng NN, Zheng SF: Learning sparse mixture models for discriminative classification . In International Journal of Pattern Recognition and Artificial Intelligence 20 (3):431-440 May 2006
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
  170. Creed F. Jones III: Color Face Recognition using Quaternionic Gabor Filters PhD Thesis, Virginia Polytechnic Institute and State University, December 2004.
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  171. Leena Lepistö: Colour and Texture Based Classification of Rock Images Using Classifier Combinations PhD Thesis, Tampere University of Technology, Tampere, Finland, 2006
    Cites Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification (JCIS 2000).
  172. Aharon Bar-Hillel: Learning from weak representations using distance functions and generative models. PhD thesis, Hebrew University of Jerusalem, October 2006.
    Cites Discriminative Training for Object Recognition Using Image Patches (CVPR 2005)
  173. J Ros: Representations structurelles parcimonieuses et monodimensionnelles des singularites d'une image Application a la classification d'images naturelles (in English!). PhD thesis, Univ. Lyon, December 2006.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  174. J Eichhorn: Applications of Kernel Machines to Structured Data. PhD thesis, TU Berlin, 2007.
    Cites Improving a Discriminative Approach to Object Recognition using Image Patches (DAGM 2005) .
  175. Gerd Brunner: Structure Features for Content-Based Image Retrieval and Classification Problems PhD Thesis, Univ. Freiburg, 2006.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
Some student papers:
  1. Sander Bockting, Matthijs Ooms: Evaluating Relevance Feedback: An Image Retrieval Interface for Children . Univ. Twente, The Netherlands, student project.
    Cites FIRE (ImageCLEF 2004).
  2. P Elango and K Jayaraman: Clustering Images Using the Latent Dirichlet Allocation Model. Univ. of Wisconsin, Madison, student project.
    Cites Features for Image Retrieval: A Quantitative Comparison. (DAGM 2004).
  3. B. Savas: Analyses and Tests of Handwritten Digit Recognition Algorithms, HTML, Master's thesis, University of Linköping, 2003.
  4. A. Doray: Tangent Distance Models, student report, Natl. Univ. of Singapore.
  5. Qing Tang: Two-dimensional penalized signal regression for hand written digit recognition. Master's Thesis, Louisiana State University, August 2006.
    Cites Experiments with an extended tangent distance (ICPR 2000).
  6. C. Lordemann, M. Lambers: Objekterkennung in Bilddaten (in German), seminar at the Univ. of Münster.
  7. M. Wu: Handwritten character recognition, Bachelor Thesis, Univ. Queensland
  8. A. Mauser: Data-Mining-Cup 2004, report on the winning method in the data mining cup 2004.
  9. VN Gudivada: Relevance Feedback in Content-Based Image Retrieval. In ????.
    Cites Classification Error Rate for Quantitative Evaluation of Content-based Image Retrieval Systems (ICPR 2004).
  10. Archias Alves de Almeida Filho: Maximizacao de Entropia em Linguistica Computacional para a Lingua Portuguesa (in Portuguese), 2002.
    Cites Maximum Entropy and Gaussian Models for Image Object Recognition (DAGM 2002).
Daniel Keysers