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Metric forests based on Gaussian mixture model for visual image classification

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Abstract

Visual image classification plays an important role in computer vision and pattern recognition. In this paper, a new random forests method called metric forests is suggested. This method takes the distribution of datasets (including the original dataset and bootstrapped ones) into full consideration. The proposed method exploits the distribution similarity between the original dataset and the bootstrapped datasets. For each bootstrapped dataset, a metric decision tree is built based on Gaussian mixture model. The metric decision tree learned from bootstrapped dataset with a low or high similarity index is given small weight when voting, vice versa. The contribution of the proposed method is originated from that the dataset with low similarity may not represent the original dataset very well while the high one with a big chance to overfit. To evaluate the proposed metric forests method, extensive of experiments was conducted for visual image classification including texture image classification, flower image classification and food image classification. The experimental results validated the superiority of the proposed metric forests on the ALOT, Flower-102 and Food-101 datasets.

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Acknowledgments

Yong Xu would like to thank the supports by National Nature Science Foundations of China (61273255 and 61070091), Engineering and 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). Lin Wang would like to thank the support by National Statistical Science Research Project of China (No. 2014LY011). 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. South China University of Technology, Guangzhou, China

    Yong Xu & Qian Zhang

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

    Qian Zhang

  3. School of Information Engineering, Guizhou Minzu University, Guiyang, China

    Lin Wang

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  1. Yong Xu
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  2. Qian Zhang
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Correspondence to Qian Zhang.

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Communicated by V. Loia.

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Xu, Y., Zhang, Q. & Wang, L. Metric forests based on Gaussian mixture model for visual image classification. Soft Comput 22, 499–509 (2018). https://doi.org/10.1007/s00500-016-2350-4

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