Advertisement

Multi-scale Attributed Graph Kernel for Image Categorization

  • Duo Hu
  • Qin XuEmail author
  • Jin Tang
  • Bin Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11258)

Abstract

The spatial pyramid matching has been widely adopted for scene recognition and image retrieval. It splits the image into sub-regions and counts the local features within the sub-region. However, it has not captured the spatial relationship between the local features located in the sub-region. This paper proposes to construct the multi-scale attributed graphs which involve the vocabulary label to characterize the spatial structure of the local features at different scales. We compute the distances of any two attributed graph corresponding to the image grids and find the optimal matching to aggregate. Then we poll the distances of graphs at different scales to build the kernel for image classification. We conduct our method on the Caltech 101, Caltech 256, Scene Categories, and Six Actions datasets and compare with five methods. The experiment results demonstrate that our method can provide a good accuracy for image categorization.

Keywords

Image classification Multi-scale attributed graph Graph distance 

Notes

Acknowledgment

The authors would like to thank the anonymous referees for their constructive comments which have helped improve the paper. The research is supported by the National Natural Science Foundation of China (Nos. 61502003, 71501002, 61472002 and 61671018), Natural Science Foundation of Anhui Province (No. 1608085QF133).

References

  1. 1.
    Penatti, O.A.B., Valle, E., da S. Torres, R.: Encoding spatial arrangement of visual words. In: San Martin, C., Kim, S.-W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 240–247. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25085-9_28CrossRefGoogle Scholar
  2. 2.
    Boureau, Y.L., Bach, F., Lecun, Y., Ponce, J.: Learning mid-level features for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 26, pp. 2559–2566 (2010)Google Scholar
  3. 3.
    Sivic, J., Russell, B.C., Efros, A.A., et al.: Discovering objects and their location in images. In: Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 370–377 (2005)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, no. (1/2), pp. 2169–2178 (2006)Google Scholar
  6. 6.
    Sadeghi, F., Tappen, M.F.: Latent pyramidal regions for recognizing scenes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 228–241. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33715-4_17CrossRefGoogle Scholar
  7. 7.
    Yang, J., Yu, K., Gong, Y., et al.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794–1801 (2009)Google Scholar
  8. 8.
    Wang, J., Yang, J., Yu, K., et al.: Locality-constrained linear coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 119, pp. 3360–3367 (2010)Google Scholar
  9. 9.
    Cao, Y., Wang, C., Li, Z., et al.: Spatial-bag-of-features. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 238, pp. 3352–3359 (2010)Google Scholar
  10. 10.
    Silva, F.B., Werneck, R.D.O., Goldenstein, S., et al.: Graph-based bag-of-words for classification. In: International Conference on Pattern Recognition, vol. 74, pp. 266–285 (2018)CrossRefGoogle Scholar
  11. 11.
    Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. In: International Conference on Pattern Recognition Letters, vol. 1, no. 4, pp. 245–253 (1983)CrossRefGoogle Scholar
  12. 12.
    Jouili, S., Mili, I., Tabbone, S.: Attributed graph matching using local descriptions. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 89–99. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04697-1_9CrossRefGoogle Scholar
  13. 13.
    Silva, F.B., Tabbone, S., Torres, R.D.S.: Bog: a new approach for graph matching. In: International Conference on Pattern Recognition, pp. 82–87 (2014)Google Scholar
  14. 14.
    Silva, F.B., Goldenstein, S., Tabbone, S., et al.: Image classification based on bag of visual graphs. In: IEEE International Conference on Image Processing, vol. 2010, pp. 4312–4316 (2014)Google Scholar
  15. 15.
    Hashimoto, M., Cesar, R.M.: Object detection by keygraph classification. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 223–232. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02124-4_23CrossRefGoogle Scholar
  16. 16.
    Zhou, F., Torre, F.D.L.: Deformable graph matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 9, pp. 2922–2929 (2013)Google Scholar
  17. 17.
    Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 42, pp. 1633–1640 (2011)Google Scholar
  18. 18.
    Zhang, L., Hong, R., Gao, Y.: Image categorization by learning a propagated graphlet path. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 674–685 (2016)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Wu, J., Pan, S., Zhu, X., et al.: Multi-graph-view learning for complicated object classification. In: International Conference on Artificial Intelligence, pp. 3953–3959. AAAI Press (2015)Google Scholar
  20. 20.
    Mousavi, S.F., Safayani, M., Mirzaei, A., et al.: Hierarchical graph embedding in vector space by graph pyramid. In: International Conference on Pattern Recognition, vol. 61, pp. 245–254 (2017)CrossRefGoogle Scholar
  21. 21.
    Nikolentzos, G., Meladianos, P., Vazirgiannis, M.: Matching node embeddings for graph similarity. In: Proceedings of the 31st Conference on Artificial Intelligence, AAAI, pp. 2429–2435 (2017)Google Scholar
  22. 22.
    Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: International Conference on World Wide Web, pp. 577–586. ACM (2011)Google Scholar
  23. 23.
    Song, J., Gao, L., Zou, F.: Deep and fast: deep learning hashing with semi-supervised graph construction. Image Vis. Comput. 55, 101–108 (2016)CrossRefGoogle Scholar
  24. 24.
    Harchaoui, Z., Bach, F.: Image classification with segmentation graph kernels. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 76, pp. 1–8 (2007)Google Scholar
  25. 25.
    Shu, X., Tang, J., Qi, G.J.: Image classification with tailored fine-grained dictionaries. IEEE Trans. Circuits Syst. Video Technol. 28(2), 454–467 (2018)CrossRefGoogle Scholar
  26. 26.
    Grauman, K., Darrell, T.: The pyramid match kernels: discriminative classification with sets of image features. In: Proceedings of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1458–1465 (2005)Google Scholar
  27. 27.
    Li, F.F., 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 on Generative-Model Based Vision, vol. 106, no. 1, pp. 59–70 (2007)Google Scholar
  28. 28.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. In: California Institute of Technology (2007)Google Scholar
  29. 29.
    Li, F.F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 524–531 (2005)Google Scholar
  30. 30.
    Li, P., Ma, J.: What is happening in a still picture? In: International Conference on Pattern Recognition, pp. 32–36 (2011)Google Scholar
  31. 31.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRefGoogle Scholar
  32. 32.
    Penatti, O.A.B., Silva, F.B., Valle, E., et al.: Visual word spatial arrangement for image retrieval and classification. In: International Conference on Pattern Recognition, vol. 47, no. 2, pp. 705–720 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina

Personalised recommendations