DocumentCode :
1570136
Title :
Video Object Segmentation Using Kernel-based Models and Spatiotemporal Similarity
Author :
Jim-Wei Hsieh ; Jun-Xian Lee
Author_Institution :
Dept. of Electr. Eng., Yuan Ze Univ., Chung-li, Taiwan
fYear :
2006
Firstpage :
1821
Lastpage :
1824
Abstract :
This paper proposes a semantic video object segmentation system which combines spatio-temporal video segmentation and region tracking together to extract important semantic objects from videos. At beginning, the paper uses multiple cues to segment video frames to different regions. The cues include color, edges, motions, and kernel-based models. Since these features are complementary to each other, all desired regions can be well segmented from input frames even though they are captured from a non-stationary camera. Then, according to temporal information of each segmented region, we can construct a region adjacency graph (RAG) which can well record the relative relations between each region. Based on the RAG, we propose a Bayesian classifier which can group regions by properly checking their spatial and temporal similarities such that different regions will be merged and associated together to form a meaningful object. Since a kernel-based analysis is included into the designed classifier, all desired semantic objects can be well extracted even though they are static in videos. Experimental results have proved the superiority of the proposed method in object segmentation.
Keywords :
Bayes methods; graph theory; image classification; image segmentation; spatiotemporal phenomena; video signal processing; Bayesian classifier; RAG; kernel-based model; region adjacency graph; semantic video object segmentation; spatiotemporal similarity; Bayesian methods; Cameras; ISO standards; Image edge detection; Image segmentation; Intelligent transportation systems; Object detection; Object segmentation; Spatiotemporal phenomena; Video compression; Object detection; Video signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2006 IEEE International Conference on
Conference_Location :
Atlanta, GA
ISSN :
1522-4880
Print_ISBN :
1-4244-0480-0
Type :
conf
DOI :
10.1109/ICIP.2006.312600
Filename :
4106906
Link To Document :
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