Graph Based Image Segmentation: A modern approach

Image segmentation refers to a process of dividing the image into disjoint regions that were meaningful. This process is fundamental in computer vision in that many applications, such as image retrieval, visual summary, image based modeling, and so on, can essentially benefit from it. This process is also challenging because the segmentation is usually subjective and the computation is highly costly. This book develops in turn the prior model for the pairwise graph approaches which is defined from multiple cues, a hyper graph based method which models multiple wise relations among the data points, and a tree structured graph based method which leads to an efficient and effective solution to the normalized cuts criterion. These approaches are demonstrated in multiple view, interactive and automatic image segmentation problems. This book is suitable for students and researchers in image processing, computer vision, pattern recognition and machine learning.

Jingdong Wang received the BSc and MSc degrees in Automation from Tsinghua University, Beijing, China, in 2001 and 2004, and the PhD degree in Computer Science from the Hong Kong University of Science and Technology, Hong Kong, in 2007. He is currently an associate researcher at the Internet Media Group, Microsoft Research Asia.