Convex hierarchical segmentation model for images with multi-component

Weibin Li; Xian Yi; Yanxia Du; Fan Zhao

doi:10.1117/12.2244304

29 August 2016 Convex hierarchical segmentation model for images with multi-component

Weibin Li, Xian Yi, Yanxia Du, Fan Zhao

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331E (2016) https://doi.org/10.1117/12.2244304
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Focus on the multi-component image segmentation issue, a hierarchical model is proposed in this paper. The idea is to do segmentation iteratively. The (k+1)-th implementation is carried out not on the whole image domain but on the subimage which is detected as the objects region at the k-th segmentation. In order to achieve this purpose of selective segmentation, a region characteristic function which takes 1 for pixel in the given region and 0 otherwise is introduced, and a novel energy function is proposed based on it. The proposed energy function is convex, thus it can easily apply the fast minimization algorithm and obtain the global minima. In this paper, the well-known split Bregman method is used to minimize the proposed energy function. Experiments demonstrate that the proposed model is able to deal with multicomponent images. And comparisons show that the model is more accurate and efficient.

Citation Download Citation

Weibin Li, Xian Yi, Yanxia Du, and Fan Zhao "Convex hierarchical segmentation model for images with multi-component", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331E (29 August 2016); https://doi.org/10.1117/12.2244304

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