Bin ratio-based histogram distances and their application to image classification

Article English OPEN
Hu, W. ; Xie, N. ; Hu, R. ; Ling, H. ; Chen, Q. ; Yan, S. ; Maybank, Stephen J. (2014)
  • Publisher: IEEE Computer Society
  • Related identifiers: doi: 10.1109/TPAMI.2014.2327975
  • Subject: csis
    acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ2 histogram distance to generate the ℓ1 BRD and the χ2 BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ2 distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.
  • References (53)
    53 references, page 1 of 6

    1. A. Agarwal and B. Triggs, “Hyperfeatures - Multilevel Local Coding for Visual Recognition,” INRIA Research Report RR-5655, 2006.

    2. A. C. Berg, T. L. Berg, and J. Malik, “Shape Matching and Object Recognition Using Low Distortion Correspondences,” in Proc. of Computer Vision and Pattern Recognition, vol. 1, pp. 26-33, 2005.

    3. D. Cai, X. He, and J. Han, “Efficient Kernel Discriminant Analysis via Spectral Regression,” In Proc. of IEEE International Conference on Data Mining, pp. 427-432, Oct. 2007.

    4. C.-K. Chiang, C.-H. Duan, S.-H. Lai, and S.-F. Chang, “Learning Component-Level Sparse Representation Using Histogram Information for Image Classification,” in Proc. of IEEE International Conference on Computer Vision, pp. 1519-1526, 2011.

    5. O. Chapelle, P. Haffner, and V. Vapnik, “Support Vector Machines for Histogram-Based Image Classification,” IEEE Trans. on Neural Networks, vol. 10, no. 5, pp. 1055-1064, Sep. 1999.

    6. O. Duchenne, F. Bach, I. Kweon, and J. Ponce, “A Tensor-Based Algorithm for High-Order Graph Matching,” in Proc. of Computer Vision and Pattern Recognition Workshops, pp. 1980-1987, 2009.

    7. V. Ablavsky and S. Sclaroff, “Learning Parameterized Histogram Kernels on the Simplex Manifold for Image and Action Classification,” in Proc. of IEEE International Conference on Computer Vision, pp. 1473-1480, 2011.

    8. M. Everingham, A. Zisserman, C. Williams, L. van Gool, M. Allan, C. Bishop, O. Chapelle, N. Dalal, T. Deselaers, and G. Dorko, “The 2005 Pascal Visual Object Classes Challenge,” Machine Learning Challenges, Lecture Notes in Computer Science, vol. 3944, pp. 117-176, 2006.

    9. L. Fei-Fei and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 524-531, vol. 2, June 2005.

    10. P. Gehler and S. Nowozin, “On Feature Combination for Multiclass Object Classification,” in Proc. of IEEE International Conference on Computer Vision, pp. 221-228, Sep. 2009.

  • Similar Research Results (1)
  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    61
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Birkbeck Institutional Research Online - IRUS-UK 0 61
Share - Bookmark