Analysis of Histogram Descriptor for Image Retrieval in DCT Domain

  • Cong Bai
  • Kidiyo Kpalma
  • Joseph Ronsin
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 11)

Abstract

Many researches of content-based image retrieval appear in transform domain. We analyze and enhance a histogram method for image retrieval in DCT domain. This approach is based on 4×4 block DCT. After pre-processing, AC and DC Patterns are extracted from DCT coefficients. After various experiments, we propose to use zig-zag scan with fewer DCT coefficients to construct the AC-Pattern. Moreover adjacent patterns are defined by observing distances between them and merged in AC-Pattern histogram. Then the descriptors are constructed from AC-Pattern and DC-Pattern histograms and the combination of these descriptors is used to do image retrieval. Performance analysis is done on two common face image databases. Experiments show that we can get better performance by using our proposals.

Keywords

Content-based image retrieval DCT zig-zag scan adjacent patterns face recognition 

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References

  1. 1.
    Tsai, T., Huang, Y.-P., Chiang, T.-W.: Image Retrieval Based on Dominant Texture Features. In: 2006 IEEE International Symposium on Indus-trial Electronics, vol. 1, pp. 441–446 (July 2006)Google Scholar
  2. 2.
    Theoharatos, C., Pothos, V.K., Laskaris, N.A., Economou, G.: Multivariate image similarity in the compressed domain using statistical graph matching. Pattern Recognition 29, 1892–1904 (2006)CrossRefGoogle Scholar
  3. 3.
    Feng, G., Jiang, J.: JPEG compressed image retrieval via statis-tical features. Pattern Recognition 36, 977–985 (2003)CrossRefGoogle Scholar
  4. 4.
    Zhong, D., Defée, I.: DCT histogram optimization for image database retrieval. Pattern Recognition Letters 26, 2272–2281 (2005)CrossRefGoogle Scholar
  5. 5.
    Bolle, R.M., Pankanti, S., Ratha, N.K.: Evaluation techniques for biomet-rics-based authentication systems (FRR). In: Proc. International Conf. on Pattern Recognition, vol. 2, pp. 831–837 (2000)Google Scholar
  6. 6.
    Daidi, Z.: Image database retrieval methods based on feature histograms. PhD thesis. Tampere University of Technology (May 2008)Google Scholar
  7. 7.
    Naz, E., Farooq, U., Naz, T.: Analysis of Principal Component Analysis-Based and Fisher Discriminant Analysis-Based Face Recognition Algorithms. In: 2006 International Conference on Emerging Technologies, pp. 121–127 (November 2006)Google Scholar
  8. 8.
    Xu, Z., Zhang, J., Dai, X.: Boosting for Learning a Similarity Measure in 2DPCA Based Face Recognition. In: 2009 World Congress on Com-puter Science and Information Engineering, vol. 7, pp. 130–134 (2009)Google Scholar
  9. 9.
    Goudelis, G., Zafeiriou, S., Tefas, A., Pitas, I.: Class-Specific Kernel-Discriminant Analysis for Face Verification. IEEE Transactions on Informa-tion Forensics and Security 2, 570–587 (2007)CrossRefGoogle Scholar
  10. 10.
    Georgia Tech Face Database, http://www.anefian.com/research/face_reco.htm (accessed March 2010)
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cong Bai
    • 1
  • Kidiyo Kpalma
    • 1
  • Joseph Ronsin
    • 1
  1. 1.INSA de Rennes, IETR, UMR 6164Université Européenne de BretagneRENNESFrance

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