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Superpixel Graph Cuts Rapid Algorithm for Extracting Object Contour Shapes
Pages: 344-353
Year: Issue:  4
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  Image SegmentationSuperpixelGraph CutsLevel SetContour Shape;
Abstract: A rapid algorithm based on level set framework is presented for extracting object contour shapes.Firstly,initial seeds are placed in an image plane evenly. Through setting superpixel evolution forces,superpixels with similar region features are generated. The image segmented by these superpixels maintains geometric characteristics of object contour shapes and in the meantime prevents overlap between superpixel regions. Secondly,based on the relationship of superpixel labeling and Heaviside function,optimization model of the Mumford-Shah energy function is built by using graph cuts. Finally,geometric shapes of the object contour can be extracted by superpixel graph cuts. Experimental results show that the number of superpixels is reduced greatly,converted optimization model satisfies requirements of graph cuts against energy function optimization, and min-cut / max-flow method does not need to solve differential equations. Higher extracting effectiveness of object contour shapes and extracting efficiency are ensured by all these measures.
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