Superpixels extracted via region fusion with boundary constraint |
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Affiliation: | 1. School of Engineering, Newcastle University, Newcastle NE1 7RU, UK;2. Institute of Neuroscience, Newcastle University, Newcastle NE2 4HH, UK;1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, PR China;2. College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410014, PR China |
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Abstract: | In this paper, we present an accurate superpixel algorithm by region fusion with boundary constraint (RFBC). Superpixels with regular shape and high boundary adherence can be generated in weak boundary and complex texture regions through our algorithm. RFBC includes two steps which are initial segmentation and region fusion respectively. In initial segmentation, broken Canny edges are connected through edge closing algorithm. Subsequently, the closed Canny edges and SLIC superpixel edges are combined together to form the incipient superpixels. In region fusion, gray Gaussian distribution and adjacent relation are used as priori to compute the degree of similarity across incipient superpixels in GBP algorithm. For concreteness, the information of similarity is propagated between regions and the most similar regions are fused, which are accomplished alternatingly to preserve accurate boundaries. Extensive experiments on the Berkeley segmentation benchmark show that the proposed algorithm outperforms the most state-of-the-art algorithms. |
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Keywords: | Superpixel Initial segmentation Edge closing Gaussian belief propagation Region fusion |
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