Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation

Image segmentation is an important processing in many applications such as image retrieval and computer vision. One of the most successful models for image segmentation is the level set methods which are based on local context. The methods, though comparatively effective in segmenting images with inhomogeneous intensity, are considerably computation-intensive and at the risk of falling into local minima in the convergence of the active contour energy function. To address the issues, we propose a region-based level set method, called KL-MLBF, which is based on the multi-scale local binary fitting (MLBF) and the Kullback-Leibler (KL) divergence. We first apply the multi-scale theory to the local binary fitting model to build MLBF. Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MLBF. KL-MLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL-MLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and real images have shown that KL-MLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating the minimization of the energy function.