Piecewise Flat Embedding for Image Segmentation
published: Feb. 10, 2016, recorded: December 2015, views: 106
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Image segmentation is a critical step in many computer vision tasks, including high-level visual recognition and scene understanding as well as low-level photo and video processing. In this paper, we propose a new nonlinear embedding, called piecewise flat embedding, for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding attempts to identify segment boundaries while significantly suppressing variations within segments. We adopt an L1-regularized energy term in the formulation to promote sparse solutions. We further devise an effective two-stage numerical algorithm based on Bregman iterations to solve the proposed embedding. Piecewise flat embedding can be easily integrated into existing image segmentation frameworks, including segmentation based on spectral clustering and hierarchical segmentation based on contour detection. Experiments on BSDS500 indicate that segmentation algorithms incorporating this embedding can achieve significantly improved results in both frameworks.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !