Advertisement

Learning Hybrid Part Filters for Scene Recognition

  • Yingbin Zheng
  • Yu-Gang Jiang
  • Xiangyang Xue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)

Abstract

This paper introduces a new image representation for scene recognition, where an image is described based on the response maps of object part filters. The part filters are learned from existing datasets with object location annotations, using deformable part-based models trained by latent SVM [1]. Since different objects may contain similar parts, we describe a method that uses a semantic hierarchy to automatically determine and merge filters shared by multiple objects. The merged hybrid filters are then applied to new images. Our proposed representation, called Hybrid-Parts, is generated by pooling the response maps of the hybrid filters. Contrast to previous scene recognition approaches that adopted object-level detections as feature inputs, we harness filter responses of object parts, which enable a richer and finer-grained representation. The use of the hybrid filters is important towards a more compact representation, compared to directly using all the original part filters. Through extensive experiments on several scene recognition benchmarks, we demonstrate that Hybrid-Parts outperforms recent state-of-the-arts, and combining it with standard low-level features such as the GIST descriptor can lead to further improvements.

Keywords

Object Class Object Part Scene Recognition Hybrid Filter Spatial Pyramid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32, 1627–1645 (2009)CrossRefGoogle Scholar
  2. 2.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)Google Scholar
  4. 4.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006)Google Scholar
  5. 5.
    Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. IJCV 72, 133–157 (2007)CrossRefGoogle Scholar
  6. 6.
    Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient Object Category Recognition Using Classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Li, L., Su, H., Xing, E., Fei-Fei, L.: Object bank: A high-level image representation for scene classification semantic feature sparsification. In: NIPS (2010)Google Scholar
  8. 8.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: ICCV (2007)Google Scholar
  9. 9.
    Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)Google Scholar
  10. 10.
    Farhadi, A., Endres, I., Hoiem, D.: Attribute-centric recognition for cross-category generalization. In: CVPR (2010)Google Scholar
  11. 11.
    Lampert, C., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)Google Scholar
  12. 12.
    Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: What helps where–and why? semantic relatedness for knowledge transfer. In: CVPR (2010)Google Scholar
  13. 13.
    Yu, X., Aloimonos, Y.: Attribute-Based Transfer Learning for Object Categorization with Zero/One Training Example. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 127–140. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Wang, G., Forsyth, D.: Joint learning of visual attributes, object classes and visual saliency. In: ICCV (2009)Google Scholar
  15. 15.
    Wang, Y., Mori, G.: A Discriminative Latent Model of Object Classes and Attributes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 155–168. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Hauptmann, A., Yan, R., Lin, W., Christel, M., Wactlar, H.: Can high-level concepts fill the semantic gap in video retrieval? A case study with broadcast news. TMM 9, 958–966 (2007)Google Scholar
  17. 17.
    Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: CVPR (2011)Google Scholar
  18. 18.
    Berg, T., Berg, A., Shih, J.: Automatic Attribute Discovery and Characterization from Noisy Web Data. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 663–676. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Parikh, D., Grauman, K.: Relative attributes. In: ICCV (2011)Google Scholar
  20. 20.
    Pandey, M., Lazebnik, S.: Scene recognition and weakly supervised object localization with deformable part-based models. In: ICCV (2011)Google Scholar
  21. 21.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  22. 22.
    Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  23. 23.
    Miller, G.: Wordnet: a lexical database for English. Communications of the ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  24. 24.
    Ozuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: CVPR (2007)Google Scholar
  25. 25.
    Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: CVPR (2009)Google Scholar
  26. 26.
    Li, L.J., Fei-Fei, L.: What, where and who? classifying events by scene and object recognition. In: ICCV (2007)Google Scholar
  27. 27.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)zbMATHCrossRefGoogle Scholar
  28. 28.
    Wu, J., Rehg, J.: CENTRIST: A visual descriptor for scene categorization. TPAMI 33, 1489–1501 (2011)CrossRefGoogle Scholar
  29. 29.
    Gao, S., Tsang, I.W., Chia, L.T., Zhao, P.: Local features are not lonely – Laplacian sparse coding for image classification. In: CVPR (2010)Google Scholar
  30. 30.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. TPAMI 30, 712–727 (2008)CrossRefGoogle Scholar
  31. 31.
    Wu, J., Rehg, J.M.: Beyond the euclidean distance: Creating effective visual codebooks using the histogram intersection kernel. In: ICCV (2009)Google Scholar
  32. 32.
    Zhou, X., Cui, N., Li, Z., Liang, F., Huang, T.: Hierarchical gaussianization for image classification. In: ICCV (2009)Google Scholar
  33. 33.
    Wang, C., Blei, D., Fei-Fei, L.: Simultaneous image classification and annotation. In: CVPR (2009)Google Scholar
  34. 34.
    Jiang, Y.G., Yang, J., Ngo, C.W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. TMM 12, 42–53 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yingbin Zheng
    • 1
  • Yu-Gang Jiang
    • 1
  • Xiangyang Xue
    • 1
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina

Personalised recommendations