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

Liu, L., Cheng, D., Tian, F., Shi, D. and Wu, R., 2017. Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation. Multimedia Tools and Applications, 76 (7), 1 0149-10168.

Full text available as:

art_10 1007_s11042-016-3603-z.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.1007/s11042-016-3603-z


© 2016 Springer Science+Business Media New YorkImage 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.

Item Type:Article
Uncontrolled Keywords:Active contour model; Energy function; Image segmentation; Kullback-Leibler divergence; Multi-scale local binary fitting
Group:Faculty of Science & Technology
ID Code:24279
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:28 Jun 2016 11:16
Last Modified:02 May 2017 14:11


Downloads per month over past year

More statistics for this item...
Repository Staff Only -