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《Acta Electronica Sinica》 2017-08
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A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering

CAI Qiang;LIU Ya-qi;CAO Jian;LI Hai-sheng;DU Jun-ping;School of Computer and Information Engineering,Beijing Technology and Business University;Beijing Key Laboratory of Big Data Technology for Food Safety;State Key Laboratory of Information Security,Institute of Information Engineering,Chinese Academy of Sciences;School of Cyber Security,University of Chinese Academy of Sciences;School of Computer Science,Beijing University of Posts and Telecommunications;  
The watershed algorithm can conduct region-based image segmentation effectively and accurately,but it tends to cause over-segmentation. To tackle the above mentioned problem,an improved watershed algorithm is proposed,as follows:first of all,the color gradient is computed using spectrum envelope filtered color image,based on which,regions with minimum gradient are marked using self-adaptive H-minima transformation method. Then,the watershed transform is applied to segment the marked gradient image. Finally,affinity propagation clustering is adopted to merge the regions segmented by the watershed transform,using color moments computed on each local region,to get the final segmentation result. Experiments conducted on public available datasets demonstrate the adaptability and robustness of proposed algorithm,compared with the relative state-ofthe-art methods. The proposed method can solve the over-segmentation problem well and get accurate results.
【Fund】: 国家自然科学基金(No.61320106006 No.61532006);; 北京市自然科学基金(No.4162019);; 北京市科技计划课题(No.Z161100001616004)
【CateGory Index】: TP391.41
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