Image Segmentation by Data Driven Markov Chain Monte Carlo

?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contributes to image segmentation in four aspects. First, it designs efficient and well-balanced Markov Chain dynamics to explore the complex solution space and, thus, achieves a nearly global optimal solution independent of initial segmentations. Second, it presents a mathematical principle and a K-adventurers algorithm for computing multiple distinct solutions from the Markov chain sequence and, thus, it incorporates intrinsic ambiguities in image segmentation. Third, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which drive the Markov chain dynamics and achieve tremendous speedup in comparison to the traditional jumpdiffusion methods [12], [11]. Fourth, the DDMCMC paradigm provides a unifying framework in which the role of many e...

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Zhuowen Tu, Song Chun Zhu, Heung-Yeung Shum
• Keywords: Computer Vision, Data-driven Markov Chain, ICCV 2001, Image Segmentation, Markov Chain Dynamics, Markov Chain Sequence, Well-balanced Markov Chain