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Spatial Statistics of Visual Keypoints for Texture Recognition

  • Huu-Giao Nguyen
  • Ronan Fablet
  • Jean-Marc Boucher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

In this paper, we propose a new descriptor of texture images based on the characterization of the spatial patterns of image keypoints. Regarding the set of visual keypoints of a given texture sample as the realization of marked point process, we define texture features from multivariate spatial statistics. Our approach initially relies on the construction of a codebook of the visual signatures of the keypoints. Here these visual signatures are given by SIFT feature vectors and the codebooks are issued from a hierarchical clustering algorithm suitable for processing large high-dimensional dataset. The texture descriptor is formed by cooccurrence statistics of neighboring keypoint pairs for different neighborhood radii. The proposed descriptor inherits the invariance properties of the SIFT w.r.t. contrast change and geometric image transformation (rotation, scaling). An application to texture recognition using the discriminative classifiers, namely: k-NN, SVM and random forest, is considered and a quantitative evaluation is reported for two case-studies: UIUC texture database and real sonar textures. The proposed approach favourably compares to previous work. We further discuss the properties of the proposed descriptor, including dimensionality aspects.

Keywords

Point Process Visual Word Training Image Texture Image Spatial Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ndjiki-Nya, P., Makai, B., Blattermann, G., Smolic, A., Schwarz, H., Wiegand, T.: A content-based video coding approach for rigid and non-rigid textures. In: IEEE Conf. on Im. Proc., ICIP, pp. 3169–3172 (2006)Google Scholar
  2. 2.
    Zalesny, A., der Maur, D.A., Paget, R., Vergauwen, M., Gool, L.V.: Realistic textures for virtual anastylosis. In: CVPR Workshop, vol. 1, pp. 14–20 (2003)Google Scholar
  3. 3.
    Karoui, I., Fablet, R., Boucher, J.M., Augustin, J.M.: Seabed segmentation using optimized statistics of sonar textures. IEEE Trans. on Geos. and Rem. Sens. 47(6), 1621–1631 (2009)CrossRefGoogle Scholar
  4. 4.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 384–393 (2002)Google Scholar
  5. 5.
    Møller, J., Syversveen, A., Waagepetersen, R.: Log gaussian cox processes. Scandinavian Journal of Statistics 25(3), 451–482 (1998)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, B.: Discovering objects and their location in images. In: ICCV, vol. 1, pp. 370–377 (2005)Google Scholar
  7. 7.
    Xu, Y., Ji, H., Fermuller, C.: Viewpoint invariant texture description using fractal analysis. IJCV 83(1), 85–100 (2009)CrossRefGoogle Scholar
  8. 8.
    Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: ICCV, vol. 1, pp. 1–8 (2007)Google Scholar
  9. 9.
    Haralick, R.: Statistical and structural approaches to textures. Proceedings of the IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  10. 10.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. on PAMI 21, 291–310 (1999)Google Scholar
  11. 11.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV 73, 213–238 (2007)CrossRefGoogle Scholar
  12. 12.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. on PAMI 27, 1265–1278 (2005)Google Scholar
  13. 13.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on PAMI 27(10), 1615–1630 (2005)Google Scholar
  15. 15.
    Linnett, L., Carmichael, D., Clarke, S.: Texture classification using a spatial-point process model. IEE Vision, Image and Signal Processing 142, 1–6 (1995)CrossRefGoogle Scholar
  16. 16.
    Goreaud, F., Pélissier, R.: On explicit formulas of edge effect correction for ripley’s k-function. Journal of Vegetation Science 10, 433–438 (1999)CrossRefGoogle Scholar
  17. 17.
    Stoyan, D., Stoyan, H.: Fractals, random shapes and point fields. Wiley, Chichester (1994)zbMATHGoogle Scholar
  18. 18.
    Nguyen, H.-G., Fablet, R., Boucher, J.-M.: Invariant descriptors of sonar textures from spatial statistics of local features. In: ICASSP, pp. 1674–1677 (2010)Google Scholar
  19. 19.
    Kotsiantis, S., Zaharakis, I., Pintelas, P.: Machine learning: a review of classification and combining techniques. Artificial Intelligence Review 26, 159–190 (2006)CrossRefGoogle Scholar
  20. 20.
    Breiman, F.: Random forests. Machine learning 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Zhao, Y., Karypis, G.: Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery 10, 141–168 (2005)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 1–22. Springer, Heidelberg (2004)Google Scholar
  23. 23.
    Chenadec, G.L., Boucher, J.M., Lurton, X.: Angular dependence of k-distributed sonar data. IEEE Trans. on Geos. and Rem. Sens. 45, 1124–1235 (2007)Google Scholar
  24. 24.
    Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide baseline stereo. IEEE Trans. on PAMI (2009)Google Scholar
  26. 26.
    Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognition 42(3), 425–436 (2009)zbMATHCrossRefGoogle Scholar
  27. 27.
    Ling, H., Soatto, S.: Proximity distribution kernels for geometric context in category recognition. In: ICCV, pp. 1–8 (2007)Google Scholar
  28. 28.
    Savarese, S., Winn, J.M., Criminisi, A.: Discriminative object class models of appearance and shape by correlatons. In: CVPR, vol. 2, pp. 2033–2040 (2006)Google Scholar
  29. 29.
    Cummins, M., Newman, P.: FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. IJRR 27, 647–665 (2008)Google Scholar
  30. 30.
    Lafarge, F., Gimel’farb, G., Descombes, X.: Geometric feature extraction by a multi-marked point process. IEEE Trans. on PAMI 99(1) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Huu-Giao Nguyen
    • 1
  • Ronan Fablet
    • 1
  • Jean-Marc Boucher
    • 1
  1. 1.Institut Telecom / Telecom Bretagne / LabSTICCUniversité européenne de BretagneBrest Cedex 3France

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