Image Object Localization Based on Multiple Image Segmentation Scoring
SHEN Xiang-Jun1, MU Lei1, ZHA Zheng-Jun2, GOU Jian-Ping1, ZHAN Yong-Zhao1
1.School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013 2.Nuclear Environment Based Teleoperation Robot Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract：Image objects localization detects object regions accurately and therefore it can improve accuracies of image objects recognition and classification. In this paper, an image object localization method based on multiple segmentation regions scoring is proposed. Through the image of the multi-level segmentation, the semantic constraints among different image regions through multi-level segmentation results is confirmed. By these constraints, frequent itemset mining and scoring strategy are applied on different levels of object region pattern. According to pattern scores of regions, important regions in each segmentation levels are merged successively to localize the whole image object region. Experimental results on MSRC and GRAZ datasets show that the proposed method can localize image foreground object accurately, and its validity is verified on Caltech256 dataset.
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2015, Vol. 28 
