Bag of textons for image segmentation via soft clustering and convex shift

We propose an unsupervised image segmentation method based on texton similarity and mode seeking. The input image is first convolved with a filter-bank, followed by soft clustering on its filter response to generate textons. The input image is then superpixelized where each belonging pixel is regarded as a voter and a soft voting histogram is constructed for each superpixel by averaging its voters’ posterior texton probabilities. We further propose a modified mode seeking method - called convex shift - to group superpixels and generate segments. The distribution of superpixel histograms is modeled nonparametrically in the histogram space, using Kullback-Leibler divergence (K-L divergence) and kernel density estimation. We show that each kernel shift step can be formulated as a convex optimization problem with linear constraints. Experiment on image segmentation shows that convex shift performs mode seeking effectively on an enforced histogram structure, grouping visually similar...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Zhiding Yu, Ang Li, Oscar C. Au, Chunjing Xu
• Keywords: Computer Vision, Convex Optimization Problem, Cvpr 2012, Kernel Density Estimation, Unsupervised Image Segmentation