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Mathematical Problems in Engineering
Volume 2016, Article ID 3180357, 11 pages
http://dx.doi.org/10.1155/2016/3180357
Research Article

Objectness Supervised Merging Algorithm for Color Image Segmentation

1The School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
2Henan Polytechnic University, Jiaozuo, China

Received 17 June 2016; Accepted 14 September 2016

Academic Editor: Wonjun Kim

Copyright © 2016 Haifeng Sima et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.