Advertisement

Scene Classification via Hypergraph-Based Semantic Attributes Subnetworks Identification

  • Sun-Wook Choi
  • Chong Ho Lee
  • In Kyu Park
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)

Abstract

Scene classification is an important issue in computer vision area. However, it is still a challenging problem due to the variability, ambiguity, and scale change that exist commonly in images. In this paper, we propose a novel hypergraph-based modeling that considers the higher-order relationship of semantic attributes in a scene and apply it to scene classification. By searching subnetworks on a hypergraph, we extract the interaction subnetworks of the semantic attributes that are optimized for classifying individual scene categories. In addition, we propose a method to aggregate the expression values of the member semantic attributes which belongs to the explored subnetworks using the transformation method via likelihood ratio based estimation. Intensive experiment shows that the discrimination power of the feature vector generated by the proposed method is better than the existing methods. Consequently, it is shown that the proposed method outperforms the conventional methods in the scene classification task.

Keywords

Scene classification Semantic attribute Hypergraph SVM 

References

  1. 1.
    von Ahn, L.: Games with a purpose. Computer 39(6), 92–94 (2006)CrossRefGoogle Scholar
  2. 2.
    Bo, L., Ren, X., Fox, D.: Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms. MIT Press (2011)Google Scholar
  3. 3.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid Generative/Discriminative approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)CrossRefGoogle Scholar
  4. 4.
    Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)CrossRefGoogle Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)Google Scholar
  6. 6.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (May 2004)Google Scholar
  7. 7.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (June 2009)Google Scholar
  8. 8.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2005)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2007 (VOC 2007) results (2007), http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/
  10. 10.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: Proc. of IEEE International Conference on Computer Vision, vol. 2, pp. 524–531 (October 2005)Google Scholar
  11. 11.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2008)Google Scholar
  12. 12.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Tech. Rep. 7694 (March 2007)Google Scholar
  13. 13.
    Kleinberg, E.M.: Stochastic discrimination. Annals of Mathematics and Artificial Intelligence 1, 207–239 (1990)CrossRefzbMATHGoogle Scholar
  14. 14.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (June 2006)Google Scholar
  15. 15.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43(1), 29–44 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Lew, M.S.: Principles of visual information retrieval. Springer, London (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Li, L.J., Fei-Fei, L.: What, where and who? classifying event by scene and object recognition. In: Proc. of IEEE International Conference on Computer Vision, pp. 1–8 (October 2007)Google Scholar
  19. 19.
    Li, L.J., Su, H., Xing, E., Fei-Fei, L.: Object bank: A high-level image representation for scene classification and semantic feature sparsification. In: Advances in Neural Information Processing Systems, pp. 1378–1386. MIT Press (2010)Google Scholar
  20. 20.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  21. 21.
    Lu, Y., Liu, P.Y., Xiao, P., Deng, H.W.: Hotelling’s t2 multivariate profiling for detecting differential expression in microarrays. Bioinformatics 21(14), 3105–3113 (2005)CrossRefGoogle Scholar
  22. 22.
    Rasiwasia, N., Vasconcelos, N.: Holistic context models for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(5), 902–917 (2012)CrossRefGoogle Scholar
  23. 23.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision 77(1-3), 157–173 (2008)CrossRefGoogle Scholar
  24. 24.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc. of IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477 (October 2003)Google Scholar
  25. 25.
    Su, Y., Jurie, F.: Improving image classification using semantic attributes. International Journal of Computer Vision 100(1), 59–77 (2012)CrossRefGoogle Scholar
  26. 26.
    Voloshin, V.I.: Introduction to graph and hypergraph theory. Nova Science Publishers, Hauppauge (2009)zbMATHGoogle Scholar
  27. 27.
    Wu, J., Rehg, J.: Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In: Proc. of IEEE International Conference on Computer Vision, pp. 630–637 (September 2009)Google Scholar
  28. 28.
    Xiao, J., Hays, J., Ehinger, K., Oliva, A., Torralba, A.: SUN database: Large-scale scene recognition from abbey to zoo. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3485–3492 (2010)Google Scholar
  29. 29.
    Zhang, B.T.: Hypernetworks: A molecular evolutionary architecture for cognitive learning and memory. IEEE Computational Intelligence Magazine 3(3), 49–63 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sun-Wook Choi
    • 1
  • Chong Ho Lee
    • 1
  • In Kyu Park
    • 1
  1. 1.Department of Information and Communication EngineeringInha UniversityIncheonKorea

Personalised recommendations