Advertisement

Sparse Representation Based Histogram in Color Texture Retrieval

  • Cong Bai
  • Jia-nan Chen
  • Jinglin ZhangEmail author
  • Kidiyo Kpalma
  • Joseph Ronsin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Sparse representation is proposed to generate the histogram of feature vectors, namely sparse representation based histogram (SRBH), in which a feature vector is represented by a number of basis vectors instead of by one basis vector in classical histogram. This amelioration makes the SRBH to be a more accurate representation of feature vectors, which is confirmed by the analysis in the aspect of reconstruction errors and the application in color texture retrieval. In color texture retrieval, feature vectors are constructed directly from coefficients of Discrete Wavelet Transform (DWT). Dictionaries for sparse representation are generated by K-means. A set of sparse representation based histograms from different feature vectors is used for image retrieval and chi-squared distance is adopted for similarity measure. Experimental results assessed by Precision-Recall and Average Retrieval Rate (ARR) on four widely used natural color texture databases show that this approach is robust to the number of wavelet decomposition levels and outperforms classical histogram and state-of-the-art approaches.

Keywords

Sparse representation Feature representation Color texture retrieval 

Notes

Acknowledgement

Part of this work was done while Cong Bai worked as a Ph.D student in IETR UMR CNRS 6164, INSA de Rennes, Université Européenne de Bretagne, France. This work is now supported by Natural Science Foundation of China under Grant No. 61502424, 61402415, U1509207 and 61325019, Zhejiang Provincial Natural Science Foundation of China under Grant No. LY15F020028, LY15F030014, LY16F020033 and Zhejiang University of Technology under Grant No.2014XZ006. The work of Jinglin Zhang is supported by the Scientific Research Foundation of Nanjing University of Information Science and Technology(Grant No.S8113055001),Natural Science Foundation of JiangSu province (Grant No.SBK2015040336) and Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase).

References

  1. 1.
    Bai, C., Zhang, J., Liu, Z., Zhao, W.L.: K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimedia Tools. Appl. 74(4), 1469–1488 (2014)CrossRefGoogle Scholar
  2. 2.
    Bai, C., Zou, W., Kpalma, K., Ronsin, J.: Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain. Electron. Lett. 48(23), 1463–1465 (2012)CrossRefGoogle Scholar
  3. 3.
    Burghouts, G.J., Geusebroek, J.M.: Material-specific adaptation of color invariant features. Pattern Recogn. Lett. 30(3), 306–313 (2009)CrossRefGoogle Scholar
  4. 4.
    Do, M., Vetterli, M.: Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans. Image Process 11(2), 146–158 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Stat. 32, 407–499 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Mairal, J., Bach, F., Ponce, J.: SPArse Modeling Software. http://spams-devel.gforge.inria.fr/index.html. Accessed June 2011
  7. 7.
    Kwitt, R., Meerwald, P., Uhl, A.: Efficient texture image retrieval using copulas in a bayesian framework. IEEE Trans. Image Process 20(7), 2063–2077 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu, G.H., Zhang, L., Hou, Y.K., Li, Z.Y., Yang, J.Y.: Image retrieval based on multi-texton histogram. Pattern Recogn. 43(7), 2380–2389 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Media Laboratory, M.: Vistex database of textures. http://vismod.media.mit.edu/vismod/imagery/VisionTexture/. Accessed Dec 2010
  11. 11.
    Mei, T., Rui, Y., Li, S., Tian, Q.: Multimedia search reranking. ACM Comput. Surv. 46(3), 1–38 (2014)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  13. 13.
    Ophir, B., Lustig, M., Elad, M.: Multi-scale dictionary learning using wavelets. IEEE J. Sel. Top. Sig. Process. 5(5), 1014–1024 (2011)CrossRefGoogle Scholar
  14. 14.
    Patel, V., Chellappa, R.: Dictionary learning. In: Patel, V.M., Chellappa, R. (eds.) Sparse Representations and Compressive Sensing for Imaging and Vision, pp. 85–92. Springer, New York (2013)CrossRefGoogle Scholar
  15. 15.
    Picard, R., Kabir, T., Liu, F.: Real-time recognition with the entire brodatz texture database. In: IEEE International Conference on Computer Vision Pattern Recognition (CVPR), pp. 638–639, June 1993Google Scholar
  16. 16.
    University of Salzburg: Salzburg texture image database. http://www.wavelab.at/sources/STex/. Accessed Sep 2012
  17. 17.
    Verdoolaege, G., De Backer, S., Scheunders, P.: Multiscale colour texture retrieval using the geodesic distance between multivariate generalized gaussian models. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 169–172, October 2008Google Scholar
  18. 18.
    Wang, M., Fu, W., Hao, S., Tao, D., Wu, X.: Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans. Knowl. Data Eng. 28(7), 1864–1877 (2016)CrossRefGoogle Scholar
  19. 19.
    Wang, M., Gao, Y., Lu, K., Rui, Y.: View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans. Image Process. 22(4), 1395–1407 (2013)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang, M., Li, H., Tao, D., Lu, K., Wu, X.: Multimodal graph-based reranking for web image search. IEEE Trans. Image Process. 21(11), 4649–4661 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang, M., Li, W., Liu, D., Ni, B., Shen, J., Yan, S.: Facilitating image search with a scalable and compact semantic mapping. IEEE Trans. Cybern. 45(8), 1561–1574 (2015)CrossRefGoogle Scholar
  22. 22.
    Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T., Yan, S.: Sparse representation for computer vision and pattern recognition. Proc. IEEE 98(6), 1031–1044 (2010)CrossRefGoogle Scholar
  23. 23.
    Zou, W., Kpalma, K., Ronsin, J.: Semantic segementation via sparse coding over hierarchical regions. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 2577–2580, October 2012Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Cong Bai
    • 1
  • Jia-nan Chen
    • 1
  • Jinglin Zhang
    • 2
    Email author
  • Kidiyo Kpalma
    • 3
  • Joseph Ronsin
    • 3
  1. 1.College of Computer ScienceZhejiang University of TechnologyHangzhouChina
  2. 2.School of Atmospheric ScienceNanjing University of Information Science and TechnologyNanjingChina
  3. 3.IETR UMR CNRS 6164, INSA de Rennes, Université Européenne de BretagneRennesFrance

Personalised recommendations