Image classification using cluster cooccurrence matrices of local relational features

ABSTRACT

Image classification systems have received a recent boost from methods using local features generated over interest points, delivering higher robustness against partial occlusion and cluttered backgrounds. We propose in this paper to use relational features calculated over multiple directions and scales around these interest points. Furthermore, a very important design issue is the choice of similarity measure to compare the bags of local feature vectors generated by each image, for which we propose a novel approach by computing image similarity using cluster co-occurrence matrices of local features. Excellent results are achieved for a widely used medical image classification task, and ideas to generalize to other tasks are discussed

REFERENCES

• 1: Shivani Agarwal , Aatif Awan , Dan Roth, Learning to Detect Objects in Images via a Sparse, Part-Based Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.26 n.11, p.1475-1490, November 2004 [doi>10.1109/TPAMI.2004.108]
• 2: J. Amores, N. Sebe, and P. Radeva. Efficient object-class recognition by boosting contextual information. In IbPRIA (1), pages 28--35, 2005.
• 3: A. Barla, F. Odone, and A. Verri. Histogram intersection kernel for image classification. In ICIP (3), pages 513--516, 2003.
• 4: S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts, 2001.
• 5: O. Chapelle, P. Haffner, and V. Vapnik. Svms for histogram-based image classification, 1999.
• 6: G. Csurka, L. Dance, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. In ECCV Workshop on statistical Learning in computer vision, pages 59--74, 2004.
• 7: L. Davis, S. Johns, and J. Aggarwal. Texture analysis using generalized co-occurrence matrices. PAMI, 3:251--259, 1979.
• 8: T. Deselaers, A. Hegerath, D. Keysers, and H. Ney. Sparse patch-histograms for object classification in cluttered images. In DAGM 2006, Pattern Recognition, 26th DAGM Symposium, Lecture Notes in Computer Science, page accepted for publication, Berlin, Germany, September 2006.
• 9: Thomas Deselaers , Daniel Keysers , Hermann Ney, Discriminative Training for Object Recognition Using Image Patches, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, p.157-162, June 20-26, 2005 [doi>10.1109/CVPR.2005.134]
• 10: L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples an incremental bayesian approach tested on 101 object categories. In Workshop on Generative-Model Based Vision, Washington, DC, June 2004.
• 11: R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proceedings of the IEEE Conference on CVPR, volume 2, pages 264--27, Madison, Wisconsin, 2003.
• 12: M. O. Gueld, M. Kohnen, D. Keysers, H. Schubert, B. B. Wein, J. Bredno, and T. M. Lehmann. Quality of DICOM header information for image categorization. In Proc. SPIE Vol. 4685, p. 280--287, Medical Imaging 2002:, pages 280--287, May 2002.
• 13: Haralick, Shanmugam, and Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610--621, 1973.
• 14: C. Hsu and C. Lin. A comparison of methods for multi-class support vector machines, 2001.
• 15: J. Huang, S. Kumar, M. Mitra, W. Zhu, and R. Zabih. Image indexing using color correlograms, 1997.
• 16: D. Keysers, C. Gollan, and H. Ney. Classification of medical images using non-linear distortion models. In Bildverarbeitung für die Medizin, pages 366--370, 2004.
• 17: Ron Kohavi , George H. John, Wrappers for feature subset selection, Artificial Intelligence, v.97 n.1-2, p.273-324, Dec. 1997 [doi>10.1016/S0004-3702(97)00043-X]
• 18: T. M. Lehmann, H. Schubert, D. Keysers, M. Kohnen, and B. B. Wein. The IRMA code for unique classification of medical images. Medical Imaging 2003: Proceedings of the SPIE, Volume 5033, pp. 440--451 (2003)., pages 440--451, May 2003.
• 19: Jia Li , James Z. Wang, Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.25 n.9, p.1075-1088, September 2003 [doi>10.1109/TPAMI.2003.1227984]
• 20: E. Loupias and N. Sebe. Wavelet-based salient points for image retrieval, 1999.
• 21: R. Marée, P. Geurts, J. Piater, and L. Wehenkel. Biomedical image classification with random subwindows and decision trees. In Proc. ICCV workshop on Computer Vision for Biomedical Image Applications (CVIBA 2005), volume 3765 of LNCS, pages 220--229. Springer-Verlag, oct 2005.
• 22: Timo Ojala , Matti Pietikäinen , Topi Mäenpää, Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns, Proceedings of the 6th European Conference on Computer Vision-Part I, p.404-420, June 26-July 01, 2000
• 23: M. Schael. Methoden zur Konstruktion invarianter Merkmale für die Texturanalyse. PhD thesis, Albert-Ludwigs-Universität, Freiburg, June 2005.
• 24: Nicu Sebe , Michael S. Lew, Comparing salient point detectors, Pattern Recognition Letters, v.24 n.1-3, p.89-96, January 2003 [doi>10.1016/S0167-8655(02)00192-7]
• 25: Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
• 26: J. Vogel. Semantic Scene Modeling and Retrieval. PhD thesis, ETH Zurich, October 2004.
• 27: Markus Weber , Max Welling , Pietro Perona, Unsupervised Learning of Models for Recognition, Proceedings of the 6th European Conference on Computer Vision-Part I, p.18-32, June 26-July 01, 2000

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
      H.3.3 Information Search and Retrieval

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.0 General
      I.5.3 Clustering
      I.5.4 Applications

General Terms:
Algorithms, Design

Keywords:
classification, image analysis, image annotation, local features