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DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions

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Abstract

As the feature dimension increases, the original pyramid matching kernel suffers from distortion factors that increase linearly with the feature dimension. This paper proposes a new method by consistently dividing the feature space into two subspaces while generating several levels. In each subspace of the level, the original pyramid matching is used. Then, a weighted sum of every subspace at each level is made as the final measurement of similarity. Experiments on data set Caltech-101 and ETH-80 show that compared with other related algorithms which need hundreds of times of original computation time, dimension partition pyramid matching kernel only needs about 4–6 times less of original computation time to obtain the similar accuracy.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (60872070); Zhejiang Province Key Scientific and Technological Project (Grant No. 2007C11094, No. 2008C21141); Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1080766).

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Authors and Affiliations

  1. College of Electric Engineering, Zhejiang University, Hangzhou, 310027, China

    Jun Zhang, Guangzhou Zhao & Hong Gu

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  1. Jun Zhang
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  2. Guangzhou Zhao
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  3. Hong Gu
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Correspondence to Jun Zhang.

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Zhang, J., Zhao, G. & Gu, H. DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions. Neural Comput & Applic 21, 1167–1175 (2012). https://doi.org/10.1007/s00521-012-0953-y

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  • DOI: https://doi.org/10.1007/s00521-012-0953-y

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