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Neural Computing and Applications

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01-01-2016 | Extreme Learning Machine and Applications

Principal pixel analysis and SVM for automatic image segmentation

Authors: Xuefei Bai, Wenjian Wang

Published in: Neural Computing and Applications | Issue 1/2016

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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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Title: Principal pixel analysis and SVM for automatic image segmentation
Authors: Xuefei Bai
Wenjian Wang
Publication date: 01-01-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 1/2016
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1544-2

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