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2006
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Video object segmentation by motion-based sequential feature clustering

10 years 8 months ago
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. Most existing methods work on image pixels or color segments which are computationally expensive. Some methods require extensive manual inputs, static cameras, and/or rigid scenes. In this paper we propose a fully automatic foreground segmentation method based on sequential clustering of sparse image features. The sparseness makes the method computationally efficient. We use both edge and corner points extracted from each video frame. A joint spatio-temporal linear regression method is developed to compute sparse motion layers of M consecutive frames jointly under the temporal consistency constraint. Once the sparse motion layers have been identified for each frame, the corresponding dense motion layers are created using the Markov Random Field (MRF) model. The MRF model assigns the rest of the im...
Mei Han, Wei Xu, Yihong Gong
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where MM
Authors Mei Han, Wei Xu, Yihong Gong
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