Image classification using cluster cooccurrence matrices of local relational features

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2006, Pages 173-182

  Setia, Lokesh ^a   Teynor, Alexandra ^a   Halawani, Alaa ^a   Burkhardt, Hans ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Classification; Image analysis; Image annotation; Local features

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS; MEDICAL IMAGING; ROBUSTNESS (CONTROL SYSTEMS); VECTOR QUANTIZATION;

CLUTTERED BACKGROUNDS; IMAGE ANNOTATION;

FEATURE EXTRACTION;

EID: 34547420130     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1178677.1178703     Document Type: Conference Paper

References (27)

1. S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via, a sparse, part-based representation. PAMI, IEEE Transactions on. 26(11):1475-1490, November 2004.
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. T. Deselaers, D. Keysers, and H. Ney. Discriminative training for object recognition using image patches. In Proceedings of the International Conference on CVPR, volume 2, pages 157-162, San Diego, CA, June 2005.
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. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
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. J. Li and J. Z. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions Pattern Analysis and Machine Intelligence, 25(9):1075-1088, 2003.
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. T. Ojala, M. Pietikäinen, and T. Mäenpää,. Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. In Proc. Sixth European Conference on Computer Vision, pages 404-420, Dublin, Ireland, 2000.
23. M. Schael. Methoden zur Konstruktion invarianter Merkmale für die Texturanalyse. PhD thesis, Albert-Ludwigs-Universität, Freiburg, June 2005.
24. N. Sebe and M. S. Lew. Comparing Salient Point Detectors. Pattern Recognition Letters, 24(1-3):89-96, January 2003.
25. V. N. Vapnik. The Nature of Statistical Learning Theory (Information Science and Statistics). Springer, 1999.
26. J. Vogel. Semantic Scene Modeling and Retrieval. PhD thesis, ETH Zurich, October 2004.
27. M. Weber, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In In Proceedings of the 6th European Conference of Computer Vision, Dublin, Ireland, 2000.