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Novel Sparse Kernel Manifold Learner for Image Classification Applications

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Recent Advances in Intelligent Image Search and Video Retrieval

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 121 ))

Abstract

This chapter presents a sparse kernel manifold learner framework for different image classification applications. First, a new DAISY Fisher vector (D-FV) feature is created by computing Fisher vectors on densely sampled DAISY features. Second, a WLD-SIFT Fisher vector (WS-FV) feature is developed by fusing the Weber local descriptors (WLD) with the SIFT descriptors, and the Fisher vectors are computed on the fused WLD-SIFT features. Third, an innovative fused Fisher vector (FFV) feature is developed by integrating the most expressive features of the D-FV, the WS-FV and the SIFT-FV features. The FFV feature is then further assessed in eight different color spaces and a novel fused color Fisher vector (FCFV) feature is computed by integrating the PCA features of the eight color FFV descriptors. Finally, we propose a sparse kernel manifold learner (SKML) method for learning a discriminative sparse representation by considering the local manifold structure and the label information based on the marginal Fisher criterion. The objective of the SKML method is to minimize the intraclass scatter and maximize the interclass separability which are defined based on the sparse criterion. The effectiveness of the proposed SKML method is assessed on different image classification datasets such as the Painting-91 dataset for computational art painting classification, the CalTech-101 dataset for object categorization, and the 15 Scenes dataset for scene classification . Experimental results show that our proposed method is able to achieve better performance than other popular image descriptors and learning methods for different visual recognition applications.

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References

  1. Amiri, S.M., Nasiopoulos, P., Leung, V.C.M.: Non-negative sparse coding for human action recognition. In: 2012 19th IEEE International Conference on Image Processing, pp. 1421–1424 (2012)

    Google Scholar 

  2. Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR ’07, pp. 401–408 (2007)

    Google Scholar 

  3. Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality sensitive discriminant analysis. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI’07, pp. 708–713 (2007)

    Google Scholar 

  4. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)

    Google Scholar 

  5. Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: Wld: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  6. Chen, S., Liu, C.: Clustering-based discriminant analysis for eye detection. IEEE Trans. Image Processing 23(4), 1629–1638 (2014)

    Article  MathSciNet  Google Scholar 

  7. 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: CVPRW, pp. 178–178 (2004)

    Google Scholar 

  8. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press Professional Inc, San Diego, CA, USA (1990)

    MATH  Google Scholar 

  9. Gao, S., Tsang, I.W.H., Chia, L.T.: Laplacian sparse coding, hypergraph laplacian sparse coding, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 92–104 (2013)

    Article  Google Scholar 

  10. Gao, Z., Liu, A., Zhang, H., Xu, G., Xue, Y.: Human action recognition based on sparse representation induced by l1/l2 regulations. In: 21st International Conference on Pattern Recognition (ICPR), pp. 1868–1871 (2012)

    Google Scholar 

  11. Goh, H., Thome, N., Cord, M., Lim, J.H.: Learning deep hierarchical visual feature coding. IEEE Trans. Neural Netw. Learn. Syst. 25(12), 2212–2225 (2014)

    Article  Google Scholar 

  12. Guo, Z., Zhang, D., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  13. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)

    Article  Google Scholar 

  14. Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)

    Article  Google Scholar 

  15. Jiang, Z., Lin, Z., Davis, L.S.: Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2651–2664 (2013)

    Article  Google Scholar 

  16. Khan, F., Beigpour, S., van de Weijer, J., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vision Appl. 25(6), 1385–1397 (2014)

    Article  Google Scholar 

  17. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on CVPR, vol. 2, pp. 2169–2178 (2006)

    Google Scholar 

  18. Li, Q., Schonfeld, D.: Multilinear discriminant analysis for higher-order tensor data classification. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2524–2537 (2014)

    Article  Google Scholar 

  19. Liu, C.: Enhanced independent component analysis and its application to content based face image retrieval. Trans. Sys. Man Cyber. Part B 34(2), 1117–1127 (2004). doi:10.1109/TSMCB.2003.821449

  20. Liu, C.: Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 572–581 (2004)

    Article  Google Scholar 

  21. Liu, C.: Extracting discriminative color features for face recognition. Pattern Recogn. Lett. 32(14), 1796–1804 (2011)

    Article  Google Scholar 

  22. Liu, C.: Discriminant analysis and similarity measure. Pattern Recogn. 47(1), 359–367 (2014)

    Article  Google Scholar 

  23. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. Trans. Img. Proc. 11(4), 467–476 (2002). doi:10.1109/TIP.2002.999679

  24. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  25. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  26. Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)

    Article  Google Scholar 

  27. Olshausen, B., Field, D.: Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Res. 37(23), 3311–3325 (1997)

    Article  Google Scholar 

  28. Peng, K.C., Chen, T.: Cross-layer features in convolutional neural networks for generic classification tasks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3057–3061 (2015)

    Google Scholar 

  29. Peng, K.C., Chen, T.: A framework of extracting multi-scale features using multiple convolutional neural networks. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2015)

    Google Scholar 

  30. Rathus, L.: Foundations of Art and Design. Wadsworth Cengage Learning, Boston, MA (2008)

    Google Scholar 

  31. van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  32. Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: CVPR, pp. 1–8 (2007)

    Google Scholar 

  33. Simonyan, K., Parkhi, O.M., Vedaldi, A., Zisserman, A.: Fisher Vector Faces in the Wild. In: BMVC (2013)

    Google Scholar 

  34. Sinha, A., Banerji, S., Liu, C.: New color gphog descriptors for object and scene image classification. Mach. Vis. Appl. 25(2), 361–375 (2014)

    Article  Google Scholar 

  35. Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)

    Article  Google Scholar 

  36. Wang, H., Yuan, C., Hu, W., Ling, H., Yang, W., Sun, C.: Action recognition using nonnegative action component representation and sparse basis selection. IEEE Trans. Image Process. 23(2), 570–581 (2014)

    Article  MathSciNet  Google Scholar 

  37. Wang, J., Wonka, P., Ye, J.: Lasso screening rules via dual polytope projection. J. Mach. Learn. Res. 16, 1063–1101 (2015)

    MathSciNet  MATH  Google Scholar 

  38. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proceedings of the IEEE Conference on CVPR, pp. 3360–3367 (2010)

    Google Scholar 

  39. Wang, J., Zhou, J., Liu, J., Wonka, P., Ye, J.: A safe screening rule for sparse logistic regression. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 1053–1061 (2014)

    Google Scholar 

  40. van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)

    Article  MathSciNet  Google Scholar 

  41. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transa. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  42. Xiang, Z.J., Ramadge, P.J.: Fast lasso screening tests based on correlations. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2137–2140 (2012)

    Google Scholar 

  43. Xiang, Z.J., Xu, H., Ramadge, P.J.: Learning sparse representations of high dimensional data on large scale dictionaries. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 900–908 (2011)

    Google Scholar 

  44. Xin, M., Zhang, H., Sun, M., Yuan, D.: Recurrent temporal sparse autoencoder for attention-based action recognition. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 456–463 (2016)

    Google Scholar 

  45. Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)

    Google Scholar 

  46. Yan, Y., Ricci, E., Subramanian, R., Liu, G., Sebe, N.: Multitask linear discriminant analysis for view invariant action recognition. IEEE Trans. Image Process. 23(12), 5599–5611 (2014)

    Article  MathSciNet  Google Scholar 

  47. Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: 2011 International Conference on Computer Vision, pp. 543–550 (2011)

    Google Scholar 

  48. Yang, M., Zhang, L., Feng, X., Zhang, D.: Sparse representation based fisher discrimination dictionary learning for image classification. Int. J. Comput. Vision 109(3), 209–232 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  49. Yuan, C., Hu, W., Tian, G., Yang, S., Wang, H.: Multi-task sparse learning with beta process prior for action recognition. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 423–429 (2013)

    Google Scholar 

  50. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833. Springer (2014)

    Google Scholar 

  51. Zhang, H., Berg, A.C., Maire, M., Malik, J.: Svm-knn: discriminative nearest neighbor classification for visual category recognition. In: Proceedings of the IEEE Conference on CVPR, vol. 2, pp. 2126–2136 (2006)

    Google Scholar 

  52. Zhang, Q., Li, B.: Discriminative k-svd for dictionary learning in face recognition. In: Proceedings of the IEEE Conference on CVPR, pp. 2691–2698 (2010)

    Google Scholar 

  53. Zhang, X., Chu, D., Tan, R.C.E.: Sparse uncorrelated linear discriminant analysis for undersampled problems. IEEE Trans. Neural Netw. Learn. Syst. 27(7), 1469–1485 (2016)

    Article  MathSciNet  Google Scholar 

  54. Zheng, J., Jiang, Z.: Learning view-invariant sparse representations for cross-view action recognition. In: 2013 IEEE International Conference on Computer Vision, pp. 3176–3183 (2013)

    Google Scholar 

  55. Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Proceedings of the NIPS, pp. 487–495 (2014)

    Google Scholar 

  56. Zhou, N., Shen, Y., Peng, J., Fan, J.: Learning inter-related visual dictionary for object recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3490–3497 (2012)

    Google Scholar 

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Authors and Affiliations

  1. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Ajit Puthenputhussery & Chengjun Liu

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  1. Ajit Puthenputhussery
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  2. Chengjun Liu
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Correspondence to Ajit Puthenputhussery or Chengjun Liu .

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Editors and Affiliations

  1. Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA

    Chengjun Liu

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Puthenputhussery, A., Liu, C. (2017). Novel Sparse Kernel Manifold Learner for Image Classification Applications. In: Liu, C. (eds) Recent Advances in Intelligent Image Search and Video Retrieval. Intelligent Systems Reference Library, vol 121 . Springer, Cham. https://doi.org/10.1007/978-3-319-52081-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-52081-0_5

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