Title: Multiple scale image segmentation-based multiple scale approximation with support vector machines

Authors: Zhaogan Lu; Chunmei Xu

Addresses: College of Computer and Information Engineering, Henan University of Economics and Laws, Zhengzhou, China ' College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China

Abstract: Aiming at the low accuracy of wavelet multiple scale segmentation with current common algorithms, one multiple scale approximation segmentation with support vector machines (SVM-MSA) is proposed in this paper. According to the proposed segmentation algorithm, the results of decomposed images at deep levels by MSA would be further segmented, which is actually mapped to the decomposed images at last levels. Then, each region from last segmentation would be further segmented according to the segmentation algorithm, and thus the segmented images with finer results. So, the nesting image segmentation could be finally obtained. Different from the classical multiple scale image segmentation algorithms, the information at the same location with different scales could be avoided to be segmented and the segmentation from fine to coarse scales could also be avoided without any amalgamation of coarse segmentation. Results of numerical evaluations showed that the consistent segmentation results could be obtained to that of human vision analysis without over segmentation of image contents.

Keywords: support vector machines; SVM; multiple scale approximation; image segmentation; wavelets.

DOI: 10.1504/IJICT.2015.070327

International Journal of Information and Communication Technology, 2015 Vol.7 No.4/5, pp.508 - 520

Received: 28 Apr 2014
Accepted: 03 Jun 2014

Published online: 15 Apr 2015 *

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