A dynamic conditional random field model for object segmentation in image sequences

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 264 - 270 vol. 1
• Main Authors: Yang Wang, Qiang Ji
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Filtering algorithms; Hidden Markov models; History; Image segmentation; Image sequences; Layout; Motion estimation; Object segmentation; Recursive estimation; Video sequences
• DOI: 10.1109/CVPR.2005.26
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919

This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of video frames. The segmentation method employs both intensity and motion cues, and it combines dynamic information and spatial interaction of the observed data. Experimental results show that the proposed approach effectively fuses contextual constraints in video sequences and improves the accuracy of object segmentation.