A Discriminative Framework for Texture and Object Recognition Using Local Image Features

  • Svetlana Lazebnik
  • Cordelia Schmid
  • Jean Ponce
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)

Abstract

This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier. Textures are represented using a visual dictionary found by quantizing appearance-based descriptors of local features. Object classes are represented using a dictionary of composite semi-local parts, or groups of nearby features with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set. Experiments on two texture and two object databases demonstrate the effectiveness of this framework for visual classification.

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References

  1. 1.
    Agarwal, S., Roth, D.: Learning a Sparse Representation for Object Detection. In: Heyden, A., et al. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 113–127. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Berg, A., Berg, T., Malik, J.: Shape Matching and Object Recognition Using Low-Distortion Correspondence. In: Proc. CVPR (2005)Google Scholar
  3. 3.
    Berger, A., Della Pietra, S., Della Pietra, V.: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics 22(1), 39–71 (1996)Google Scholar
  4. 4.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover, New York (1966)Google Scholar
  5. 5.
    Chen, S., Goodman, J.: An Empirical Study of Smoothing Techniques for Language Modeling. In: Proc. Conf. of the Association for Computational Linguistics, pp. 310–318 (1996)Google Scholar
  6. 6.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual Categorization with Bags of Keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  7. 7.
    Dorko, G., Schmid, C.: Selection of Scale-Invariant Parts for Object Class Recognition. In: Proc. ICCV, vol.I, pp. 634–640 (2003)Google Scholar
  8. 8.
    Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: Proc. CVPR 2003, vol. II, pp. 264–271 (2003)Google Scholar
  9. 9.
    Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous object recognition and segmentation by image exploration. In: Proc. ECCV (2004)Google Scholar
  10. 10.
    Jeon, J., Manmatha, R.: Using maximum entropy for automatic image annotation. In: Enser, P.G.B., et al. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 24–32. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Jurie, F., Schmid, C.: Scale-invariant Shape Features for Recognition of Object Categories. In: Proc. CVPR (2004)Google Scholar
  12. 12.
    Keysers, D., Och, F., Ney, H.: Maximum Entropy and Gaussian Models for Image Object Recognition. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, p. 498. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Lazebnik, S., Schmid, C., Ponce, J.: A Sparse Texture Representation Using Local Affine Regions. IEEE Trans. PAMI 27(8), 1265–1278 (2005)Google Scholar
  14. 14.
    Lazebnik, S., Schmid, C., Ponce, J.: A Maximum Entropy Framework for Part-Based Texture and Object Recognition. In: Proc. ICCV 2005 (to appear, 2005)Google Scholar
  15. 15.
    Lazebnik, S., Schmid, C., Ponce, J.: Semi-local Affine Parts for Object Recognition. In: Proc. BMVC 2004 (2004)Google Scholar
  16. 16.
    Lindeberg, T.: Feature Detection with Automatic Scale Selection. IJCV 30(2), 77–116 (1998)Google Scholar
  17. 17.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Mahamud, S., Hebert, M., Lafferty, J.: Combining Simple Discriminators for Object Discrimination. In: Heyden, A., et al. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 776–790. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  19. 19.
    McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI 1998 Workshop on Learning for Text Categorization, pp. 41–48 (1998)Google Scholar
  20. 20.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Proc. CVPR 2003, vol. 2, pp. 257–263 (2003)Google Scholar
  21. 21.
    Nigam, K., Lafferty, J., McCallum, A.: Using Maximum Entropy for Text Classification. In: IJCAI Workshop on Machine Learning for Information Filtering, pp. 61–67 (1999)Google Scholar
  22. 22.
    Rothganger, F., Lazebnik, S., Schmid, C., Ponce, J.: 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. In: IJCV (to appear, 2005)Google Scholar
  23. 23.
    Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: Proc. ICCV 2003, pp. 1470–1477 (2003)Google Scholar
  24. 24.
    Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: Proc. ICCV 2005 (to appear, 2005)Google Scholar
  25. 25.
    Varma, M., Zisserman, A.: Texture Classification: Are Filter Banks Necessary? In: Proc. CVPR 2003, vol. 2, pp. 691–698 (2003)Google Scholar
  26. 26.
    Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  27. 27.
    Willamowski, J., Arregui, D., Csurka, G., Dance, C.R., Fan, L.: Categorizing nine visual classes using local appearance descriptors. In: International Workshop on Learning for Adaptable Visual Systems (2004)Google Scholar
  28. 28.
    Zhu, S.C., Wu, Y.N., Mumford, D.: Filters, Random Fields, and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. IJCV 27(2), 1–20 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Svetlana Lazebnik
    • 1
  • Cordelia Schmid
    • 2
  • Jean Ponce
    • 1
  1. 1.Beckman InstituteUniversity of IllinoisUrbanaUSA
  2. 2.INRIA Rhône-AlpesMontbonnotFrance

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