Special Section on Ultrawide Context- and Content-Aware Imaging Part II

Multiresolution saliency map based object segmentation

[+] Author Affiliations
Jian Yang, Xin Wang, ZhenYou Dai

Harbin Institute of Technology ShenZhen Graduate School, School of Mechanical Engineering and Automation, D414, HIT Campus, University Town, ShenZhen 518055, China

J. Electron. Imaging. 24(6), 061205 (Nov 17, 2015). doi:10.1117/1.JEI.24.6.061205
History: Received May 25, 2015; Accepted October 15, 2015
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Abstract.  Salient objects’ detection and segmentation are gaining increasing research interest in recent years. A saliency map can be obtained from different models presented in previous studies. Based on this saliency map, the most salient region (MSR) in an image can be extracted. This MSR, generally a rectangle, can be used as the initial parameters for object segmentation algorithms. However, to our knowledge, all of those saliency maps are represented in a unitary resolution although some models have even introduced multiscale principles in the calculation process. Furthermore, some segmentation methods, such as the well-known GrabCut algorithm, need more iteration time or additional interactions to get more precise results without predefined pixel types. A concept of a multiresolution saliency map is introduced. This saliency map is provided in a multiresolution format, which naturally follows the principle of the human visual mechanism. Moreover, the points in this map can be utilized to initialize parameters for GrabCut segmentation by labeling the feature pixels automatically. Both the computing speed and segmentation precision are evaluated. The results imply that this multiresolution saliency map-based object segmentation method is simple and efficient.

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Citation

Jian Yang ; Xin Wang and ZhenYou Dai
"Multiresolution saliency map based object segmentation", J. Electron. Imaging. 24(6), 061205 (Nov 17, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061205


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