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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 174709, 12 pages
Research Article

Automatic Segmentation of Nature Object Using Salient Edge Points Based Active Contour

1Faculty of Computer Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
2Jiangsu Provincial Key Laboratory for Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai’an 223003, China
3The School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China

Received 8 September 2014; Revised 14 December 2014; Accepted 15 December 2014

Academic Editor: Erik Cuevas

Copyright © 2015 Shangbing Gao 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.


Natural image segmentation is often a crucial first step for high-level image understanding, significantly reducing the complexity of content analysis of images. LRAC may have some disadvantages. (1) Segmentation results heavily depend on the initial contour selection which is a very skillful task. (2) In some situations, manual interactions are infeasible. To overcome these shortcomings, we propose a novel model for unsupervised segmentation of viewer’s attention object from natural images based on localizing region-based active model (LRAC). With aid of the color boosting Harris detector and the core saliency map, we get the salient object edge points. Then, these points are employed as the seeds of initial convex hull. Finally, this convex hull is improved by the edge-preserving filter to generate the initial contour for our automatic object segmentation system. In contrast with localizing region-based active contours that require considerable user interaction, the proposed method does not require it; that is, the segmentation task is fulfilled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmentation methods, yielding higher precision and better recall rates. Our framework can reliably and automatically extract the object contour from the complex background.