Motion segmentation in compressed video using Markov Random Fields

In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First, motion vectors (MVs) are quantized into several representative classes, from which MRF priors are estimated. Then, a coarse segmentation map of the MV field is obtained using a maximum a posteriori estimate of the MRF label process. Finally, the boundaries of segmented moving regions are refined using color and edge information. The algorithm has been validated on a number of test sequences, and experimental results are provided to demonstrate its superiority over state-of-the-art methods. Keywords-- Motion segmentation, Markov Random Field, compressed video

• Type: Conference
• Year: 2010
• Where: ICMCS
• Authors: Yue-Meng Chen, Ivan V. Bajic, Parvaneh Saeedi
• Keywords: Coarse Segmentation Map, ICMCS 2010, Markov Random Field, Multimedia, Unsupervised Segmentation Algorithm