DocumentCode :
2490795
Title :
Experts-Shift: Learning active spatial classification experts for keyframe-based video segmentation
Author :
Zhao, Yibiao ; Duan, Yanbiao ; Nie, Xiaohan ; Huang, Yaping ; Luo, Siwei
Author_Institution :
Beijing Jiaotong Univ., Beijing, China
fYear :
2011
fDate :
5-7 Jan. 2011
Firstpage :
622
Lastpage :
627
Abstract :
Experts-Shift is a novel statistical framework for keyframe-based video segmentation. Compared to existing video segmentation techniques with simple color models, our method proposes a probability mixture model coupling strong image classifiers (experts) with latent spatial configuration. In order to propagate image labels to the successive frames, our algorithm track all experts jointly by a efficient MCMC sampler with their relations modeled by MRFs. This algorithm is capable to handle overlapping color distribution, ambiguous image boundaries, large displacement in challenging scenario with a solid foundation of both generative modeling and discriminative learning. Experiment shows our algorithm achieves high quality results and need less supervision than previous work.
Keywords :
Markov processes; Monte Carlo methods; image classification; image colour analysis; image segmentation; learning (artificial intelligence); video signal processing; MCMC sampler; color models; discriminative learning; experts-shift; generative modeling; image classifiers; keyframe-based video segmentation; learning active spatial classification experts; probability mixture model coupling; Computer vision; Image color analysis; Image segmentation; Joints; Pixel; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applications of Computer Vision (WACV), 2011 IEEE Workshop on
Conference_Location :
Kona, HI
ISSN :
1550-5790
Print_ISBN :
978-1-4244-9496-5
Type :
conf
DOI :
10.1109/WACV.2011.5711562
Filename :
5711562
Link To Document :
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