Cross-Codebook Image Classification

  • Shuai Liao
  • Xirong Li
  • Xiaoyong Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8294)


Representing images by bag of visual codes (BoVC) features has been the cornerstone of state-of-the-art image classification system. Since the BoVC features depend on a precomputed codebook in use, when the codebook applied to test images differs from the codebook of an existing image classification system, the system becomes inapplicable. To resolve the codebook incompatibility problem, we propose in this paper cross-codebook image classification. This is achieved by transforming BoVC features derived from one codebook to make them compatible with another codebook. Two BoVC transform methods, i.e., code-reassignment and least squares, are studied. Experiments on a popular image classification benchmark set show that both methods are better than random guess when crossing the codebooks. In particular, when the BoVC features are transformed from a higher dimension to a relatively small dimension, cross-codebook image classification has a similar performance compared to within-codebook image classification, with a relative performance loss of 1.3% only. The results justify the feasibility of the proposed cross-codebook image classification.


Image classification bag of visual codes cross-codebook 


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  1. 1.
    Perronnin, F., Akata, Z., Harchaoui, Z., Schmid, C.: Towards good practice in large-scale learning for image classification. In: CVPR, pp. 3482–3489 (2012)Google Scholar
  2. 2.
    Jiang, Y.G., Yang, J., Ngo, C.W., Hauptmann, A.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Transactions on Multimedia 12(1), 42–53 (2010)CrossRefGoogle Scholar
  3. 3.
    van de Sande, K., Gevers, T., Snoek, C.: Empowering visual categorization with the gpu. IEEE Transactions on Multimedia 13(1), 60–70 (2011)CrossRefGoogle Scholar
  4. 4.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, vol. 1, p. 22 (2004)Google Scholar
  5. 5.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y., Zheng, Y.: Nus-wide: a real-world web image database from national university of singapore. In: CIVR, p. 48 (2009)Google Scholar
  7. 7.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2001)Google Scholar
  8. 8.
    Nowak, S., Huiskes, M.: New strategies for image annotation: Overview of the photo annotation task at Image CLEF 2010. In: CLEF (2010)Google Scholar
  9. 9.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR, pp. 1–8. IEEE (2008)Google Scholar
  10. 10.
    Li, X., Snoek, C., Worring, M., Koelma, D., Smeulders, A.: Bootstrapping visual categorization with relevant negatives. IEEE Transactions on Multimedia 15(4), 933–945 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Shuai Liao
    • 1
    • 2
  • Xirong Li
    • 1
    • 2
  • Xiaoyong Du
    • 1
    • 2
    • 3
  1. 1.Key Lab of Data Engineering and Knowledge EngineeringMOEChina
  2. 2.School of InformationRenmin University of ChinaBeijingChina
  3. 3.State Key Lab of Software Development EnvironmentBeijingChina

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