Clothing Extraction Using Region-Based Segmentation and Pixel-Level Refinement
In this paper, we demonstrate an effective method for automatic extracting clothing object from fashion photographs, an extremely challenging problem due to the non-uniform natural backgrounds, various types of apparel and different poses of human models. This method consists of three phases: (1) coarse clothing area localization by pose estimation and super pixel segmentation, (2) region-level image segmentation, (3) pixel-level refinement using spatial information and Grab cut. Experiments on a dataset with 1000 images crawled from Taobao demonstrate that the proposed method outperforms other methods, which can extract clothing from images with complex background.