Abstract
The purpose of the study was to modify Chan --- Vese algorithm in order to overcome its shortcomings, such as high computational complexity and the use of approximations. In the considered modification, optimization is carried out by the majorization-minimization method, the main idea of which is to reduce the complexity of the problem using the majority function. Due to the proposed optimization method, it is possible to use the Heaviside step function and Dirac delta function. This enabled the same or better saturation levels when optimization is done by the graph cut method in a smaller number of iterations, which reduced the operation time. The proposed algorithm was tested on a Caltech101 dataset. The algorithm is general, does not depend on the subject area and does not require prior training. This allows it to be used as the basis for a wide range of image segmentation algorithms.
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