Date of Publication :7th March 2016

Abstract: A comparative analysis to fast multi label color image segmentation using Convex optimization techniques were studied. The presented model is in some ways related to the well-known Mumford–Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. It represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive. The nontrivial transformation of this model into a convex–concave saddle point problem, and the numerical treatment of the problem were studied. By applying an algorithm to various images it shows that the results are competitive in terms of quality at unprecedentedly low computation times. This algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images.

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