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Block-based selection random forest for texture classification using multi-fractal spectrum feature

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Abstract

This paper proposes a block-based selection random forest (BBSRF) for texture classification task using multi-fractal spectrum (MFS) feature descriptor. The random feature selection method for node splitting in random forest may omit some features which would be informative and critical to represent the instances. The BBSRF ensures that each feature would be considered via the block-based selection strategy. In BBSRF, all features are divided into \(k\) blocks; next, we generate synthesis feature subset which is made up of all features in one block and \(m\) random features from the remaining \((k-1)\) blocks; finally, each node splitting of the random tree is operated on one synthesis feature subset. After all blocks have been searched, all features are re-divided into new \(k\) blocks. The above process works iteratively until the satisfactory result is obtained. Once the random trees have been built, a testing instance is classified by voting from them. We conducted the experiments on five texture benchmark datasets with the help of MFS feature. Experimental results demonstrate the excellent performance of the proposed method in comparison with state-of-the-art results on these datasets.

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Notes

  1. http://www-cvr.ai.uiuc.edu/ponce_grp/data/.

  2. http://www.cfar.umd.edu/~fer/website-texture/texture.htm.

  3. http://www.nada.kth.se/cvap/databases/kth-tips/.

  4. http://aloi.science.uva.nl/public_alot/.

  5. http://people.csail.mit.edu/celiu/CVPR2010/FMD/.

  6. http://www1.cs.columbia.edu/CAVE/software/curet/html/download.html.

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Acknowledgments

Yong Xu would like to thank the supports by National Nature Science Foundations of China (61273255 and 61070091), Engineering & Technology Research Center of Guangdong Province for Big Data Analysis and Processing ([2013]1589-1-11), Project of High Level Talents in Higher Institution of Guangdong Province (2013-2050205-47) and Guangdong Technological Innovation Project (2013KJCX0010). Qian Zhang would like to thank the support by Guizhou Province Science and Technology Project (QIAN KE HE J ZI[2014]2094).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Qian Zhang & Yong Xu

  2. Academic Affairs Office, Guizhou Minzu University, Guiyang, 550025, China

    Qian Zhang

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  1. Qian Zhang
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Correspondence to Yong Xu.

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Zhang, Q., Xu, Y. Block-based selection random forest for texture classification using multi-fractal spectrum feature. Neural Comput & Applic 27, 593–602 (2016). https://doi.org/10.1007/s00521-015-1880-5

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