Notice

 
Abstract
Image and video segmentation is the process to separate objects from the background in still images or video sequences. It is applicable as a preliminary to various vision applications, such as action recognition, content-based image and video retrieval, targeted content replacement, and image and video summarization. It is hence important to develop efficient image and video segmentation techniques. However, image and video segmentation is challenging due to a variety of difficulties, including boundary ambiguity, cluttered background, occlusion, and non-rigid object deformation.
In this lecture, we discuss two related energy terms, called repulsive restart rule and antagonistic energy term, that can be effectively employed for successful image and video segmentation.
First, we propose a graph-based system to simulate the movements and interactions of multiple random walkers (MRW). In the MRW system, multiple agents traverse a single graph simultaneously. To achieve desired interactions among those agents, a restart rule can be designed, which determines the restart distribution of each agent according to the probability distributions of all agents. In particular, we develop the repulsive rule for data clustering. We illustrate that the MRW clustering with repulsive rule can segment real images and videos reliably.
Second, we propose an unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.
 
Biography
Chang-Su Kim received the Ph.D. degree in electrical engineering from Seoul National University with a Distinguished Dissertation Award in 2000. From 2000 to 2001, he was a Visiting Scholar with the Signal and Image Processing Institute, University of Southern California, Los Angeles. From 2001 to 2003, he coordinated the 3D Data Compression Group in National Research Laboratory for 3D Visual Information Processing in SNU. From 2003 and 2005, he was an Assistant Professor in the Department of Information Engineering, Chinese University of Hong Kong. In Sept. 2005, he joined the School of Electrical Engineering, Korea University, where he is a Professor. His research topics include image processing and computer vision. In 2009, he received the IEEK/IEEE Joint Award for Young IT Engineer of the Year. In 2014, he received the Best Paper Award from Journal of Visual Communication and Image Representation (JVCI). He has published more than 240 technical papers in international journals and conferences. He served as an Editorial Board Member of JVCI and an Associate Editor of IEEE Transactions on Image Processing. He is a Senior Area Editor of JVCI and an Associate Editor of IEEE Transactions on Multimedia.