An Auto-Segmentation Algorithm for Multi-Label Image Based on Graph Cut

Abstract

The image segmentation algorithm based on graph cut guarantees a globally optimal solution for energy solution, which is usually with the aid of user’s interactive operation. For the multi-label image segmentation application, the graph cut algorithm has two drawbacks. Firstly, it has a higher computational complexity of segment multi-label images. Secondly, it is prone to be trapped in local minima when solves the energy formulation. For the two drawbacks, this paper presents an auto-segmentation algorithm based on graph cut to segment multi-label images. The number of the labels is obtained via the main colors of the image, then the main colors are employed as pre-specified nodes feature, rather than select seeds with the aid of prior knowledge or initialization operation by the user. The seeds can be selected automatically without complex mathematical formulations to computerize, and it reduces the computational complexity successfully, and avoids falling into local minima effectively. In additional, we use a fast α-expansion move algorithm to optimize the energy function, which can improve the speed of segmentation. Comparing the proposed algorithm with the state-of-the-art segmentation methods, the experimental results show that the proposed algorithm has superior performance.