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Principal pixel analysis and SVM for automatic image segmentation

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Abstract

Segmenting objects from images is an important but highly challenging problem in computer vision and image processing. This paper presents an automatic object segmentation approach based on principal pixel analysis (PPA) and support vector machine (SVM), namely PPA–SVM. The method comprises three main steps: salient region extraction, principal pixel analysis, as well as SVM training and segmentation. We consider global saliency information and color feature by means of visual saliency detection and histogram analysis, such that SVM training data can be selected automatically. Experiment results on a public benchmark dataset demonstrate that, compared with some classical segmentation algorithms, the proposed PPA–SVM method can effectively segment the whole salient object with reasonable better performance and faster speed.

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Acknowledgments

The work described in this paper was partially supported by the National Natural Science Foundation of China (No. 61273291), Research Project Supported by Shanxi Scholarship Council of China (No. 2012-008), Scientific and Technological Project of Shanxi Province (No. 20120321027-01), Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (No. CAAC-ITRB-201305).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, People’s Republic of China

    Xuefei Bai & Wenjian Wang

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, People’s Republic of China

    Wenjian Wang

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  1. Xuefei Bai
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  2. Wenjian Wang
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Correspondence to Wenjian Wang.

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Bai, X., Wang, W. Principal pixel analysis and SVM for automatic image segmentation. Neural Comput & Applic 27, 45–58 (2016). https://doi.org/10.1007/s00521-013-1544-2

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