An Impact of PCA-Mixture Models and Different Similarity Distance Measure Techniques to Identify Latent Image Features for Object Categorization

  • K. Mahantesh
  • V. N. Manjunath Aradhya
  • C. Naveena
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)

Abstract

In the current image retrieval systems, there exists a problem of defining and identifying efficient features in order to successfully bridge the gap between low level and high level semantics. In this regard, we propose an approach of efficiently extracting semantic features by combination of EM algorithm and PCA techniques, and thereby exploring PCA-Mixture Model with various similarity techniques for image retrieval system. Firstly, Expectation Maximization (EM) algorithm is applied to learn mixture of eigen values to obtain optimized maximum likelihood clusters. secondly, Principal Component Analysis (PCA) is applied for different mixtures in order to extract efficient features. Further classification is performed using five different distance metrics. Our proposed method reported state-of-the-art classification rate with lesser features and achieved promising results in classifying Caltech-101 object categories compared with other baseline methods performed on the same dataset.

Keywords

Image Retrieval latent variable EM algorithm PCA Similar distance metrics Semantic gap 

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References

  1. 1.
    Enser, P.: Visual image retrieval: seeking the alliance of concept-based and Content-based paradigms. Journal of Information Science 26(4), 199–210 (2000)CrossRefGoogle Scholar
  2. 2.
    Enser, P.G.B.: Pictorial information retrieval. Journal of Document 51(2), 126–170 (1995)CrossRefGoogle Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
  4. 4.
    Stricker, M., Orengo, M.: Similarity of color images. SPIE Storage and Retrieval for Image and Video Databases III 2185, 381–392 (1995)CrossRefGoogle Scholar
  5. 5.
    Pass, G., Zabith, R.: Histogram refinement for content-based image retrieval. In: IEEE Workshop on Applications of Computer Vision, pp. 96–102 (1996)Google Scholar
  6. 6.
    Huang, J., et al.: Image indexing using color correlogram. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, Puerto Rico, pp. 762–768 (June 1997)Google Scholar
  7. 7.
    Zhang, J., Tan, T., Ma, L.: Invariant texture segmentation via circular Gabor filters. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 901–904 (2002)Google Scholar
  8. 8.
    Cui, P., Li, J., Pan, Q., Zhang, H.: Rotation and scaling invariant texture classification based on radon transform and multiscale analysis. Pattern Recognition Letters 27(5), 408–413 (2006)CrossRefGoogle Scholar
  9. 9.
    Krishnamoorthi, R., Sathiya devi, S.: devi, A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model. J. Vis. Commun. Image R. 23, 18–30 (2012)CrossRefGoogle Scholar
  10. 10.
    Xing-Yuan, W., Zhi-Feng, C., Jiao-Jiao, Y.: An effective method for color image retrieval based on texture. Computer Standards & Interfaces 34, 31–35 (2012)CrossRefGoogle Scholar
  11. 11.
    Yamada, A., Pickering, M., Jeannin, S., Jens, L.C.: MPEG-7 visual part of experimentation model version 9.0-part 3 dominant color. ISO/IEC JTC1/SC29/WG11/N3914, Pisa (2001)Google Scholar
  12. 12.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer (1995)Google Scholar
  13. 13.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: IEEE CVPR Workshop of Generative Model Based Vision (WGMBV) (2004)Google Scholar
  14. 14.
    Grauman, K., Darrell, T.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: The Proceedings of ICCV, vol. 2, pp. 1458–1465. IEEE (2005)Google Scholar
  15. 15.
    Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. In: CVPR, vol. 2, pp. 2126–2136. IEEE (2006)Google Scholar
  16. 16.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: CVPR, vol. 2, pp. 2169–2178. IEEE (2006)Google Scholar
  17. 17.
    Mutch, J., Lowe, D.G.: Multiclass Object Recognition with Sparse, Localized Features. In: CVPR, vol. 1, pp. 11–18. IEEE (2006)Google Scholar
  18. 18.
    Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: CVPR, San Diego (June 2005)Google Scholar
  19. 19.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  20. 20.
    Roweis, S.: Em algorithms for pca and spca. Advances in Neural Information Processing Systems 10, 626–632 (1997)Google Scholar
  21. 21.
    Bilmes, J.A.: A Gentle Tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and HMM. In: Intl. CSI, Berkeley, CA, pp. 1–13 (1998)Google Scholar
  22. 22.
    Holub, A., Welling, M., Perona, P.: Exploiting unlabelled data for hybrid object classification. In: NIPS Workshop on Inter-Class Transfer, Whistler, B.C (December 2005)Google Scholar
  23. 23.
    Kim, H.C., Kim, D., Bang, S.Y.: Face recognition using the mixture-of-eigen faces method. Pattern Recognition Letters 23, 1549–1558 (2002)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • K. Mahantesh
    • 1
  • V. N. Manjunath Aradhya
    • 2
  • C. Naveena
    • 3
  1. 1.Department of ECESri Jagadguru Balagangadhara Institute of TechnologyBangaloreIndia
  2. 2.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia
  3. 3.Department of CSEHKBK College of EngineeringBangaloreIndia

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