Novel Gabor-PHOG Features for Object and Scene Image Classification

  • Atreyee Sinha
  • Sugata Banerji
  • Chengjun Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

A new Gabor-PHOG (GPHOG) descriptor is first introduced in this paper for image feature extraction by concatenating the Pyramid of Histograms of Oriented Gradients (PHOG) of all the local Gabor filtered images. Next, a comparative assessment of the classification performance of the GPHOG descriptor is made in six different color spaces, namely the RGB, HSV, YCbCr, oRGB, DCS and YIQ color spaces, to propose the novel YIQ-GPHOG and the YCbCr-GPHOG feature vectors that perform well on different object and scene image categories. Third, a novel Fused Color GPHOG (FC-GPHOG) feature is presented by integrating the PCA features of the six color GPHOG descriptors for object and scene image classification, with applications to image search and retrieval. Finally, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The effectiveness of the proposed feature vectors for image classification is evaluated using two grand challenge datasets, namely the Caltech 256 dataset and the MIT Scene dataset.

Keywords

Gabor-PHOG (GPHOG) YIQ-GPHOG YCbCr-GPHOG FC-GPHOG PCA EFM color spaces image search 

References

  1. 1.
    Gonzalez, R., Woods, R.: Digital Image Processing. Prentice-Hall (2001)Google Scholar
  2. 2.
    Banerji, S., Verma, A., Liu, C.: Novel color LBP descriptors for scene and image texture classification. In: 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, July 18-21 (2011)Google Scholar
  3. 3.
    Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. International Journal of Pattern Recognition and Artificial Intelligence 19(7) (2005)Google Scholar
  4. 4.
    Verma, A., Banerji, S., Liu, C.: A new color SIFT descriptor and methods for image category classification. In: International Congress on Computer Applications and Computational Science, Singapore, December 4-6, pp. 819–822 (2010)Google Scholar
  5. 5.
    Burghouts, G., Geusebroek, J.M.: Performance evaluation of local color invariants. Computer Vision and Image Understanding 113, 48–62 (2009)CrossRefGoogle Scholar
  6. 6.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: International Conference on Image and Video Retrieval, Amsterdam, The Netherlands, July 9-11, pp. 401–408 (2007)Google Scholar
  7. 7.
    Marcelja, S.: Mathematical description of the responses of simple cortical cells. Journal of the Optical Society of America 70, 1297–1300 (1980)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Daugman, J.: Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research 20, 847–856 (1980)CrossRefGoogle Scholar
  9. 9.
    Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 974–989 (1999)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Wechsler, H.: Robust coding schemes for indexing and retrieval from large face databases. IEEE Transactions on Image Processing 9(1), 132–137 (2000)CrossRefGoogle Scholar
  11. 11.
    Bratkova, M., Boulos, S., Shirley, P.: oRGB: A practical opponent color space for computer graphics. IEEE Computer Graphics and Applications 29(1), 42–55 (2009)CrossRefGoogle Scholar
  12. 12.
    Liu, C.: Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Transactions on Information Forensics and Security 3(2), 213–222 (2008)CrossRefGoogle Scholar
  13. 13.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press (1990)Google Scholar
  14. 14.
    Lee, H., Chung, Y., Kim, J., Park, D.: Face Image Retrieval Using Sparse Representation Classifier with Gabor-LBP Histogram. In: Chung, Y., Yung, M. (eds.) WISA 2010. LNCS, vol. 6513, pp. 273–280. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), Washington, DC, USA, vol. 1, pp. 886–893 (2005)Google Scholar
  16. 16.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, vol. 2 (2006)Google Scholar
  17. 17.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007)Google Scholar
  18. 18.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Atreyee Sinha
    • 1
  • Sugata Banerji
    • 1
  • Chengjun Liu
    • 1
  1. 1.Department of Computer ScienceNew Jersey Institute of TechnologyNewarkUSA

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