Spatiotemporal segmentation for compact video representation

In this paper, a novel hierarchical object-oriented video segmentation and representation algorithm is proposed. The local variance contrast and the frame difference contrast are jointly exploited for structural spatiotemporal video segmentation because these two visual features can indicate the spa...

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Bibliographic Details
Published in:Signal processing. Image communication 2001-02, Vol.16 (6), p.553-566
Main Authors: Fan, Jianping, Yu, Jun, Fujita, Gen, Onoye, Takao, Wu, Lide, Shirakawa, Isao
Format: Article
Language:English
Subjects:
Applied sciences
Computers in experimental physics
Entropic thresholding
Exact sciences and technology
Image processing
Information, signal and communications theory
Instruments, apparatus, components and techniques common to several branches of physics and astronomy
Physics
Signal processing
Similarity measure
Spatiotemporal video segmentation
Telecommunications and information theory
Temporal tracking
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Summary:In this paper, a novel hierarchical object-oriented video segmentation and representation algorithm is proposed. The local variance contrast and the frame difference contrast are jointly exploited for structural spatiotemporal video segmentation because these two visual features can indicate the spatial homogeneity of the grey levels and the temporal coherence of the motion fields efficiently, where the two-dimensional (2D) spatiotemporal entropic technique is further selected for generating the 2D thresholding vectors adaptively according to the variations of the video components. After the region growing and edge simplification procedures, the accurate boundaries among the different video components are further exploited by an intra-block edge extraction procedure. Moreover, the relationships of the video components among frames are exploited by a temporal tracking procedure. This proposed object-oriented spatiotemporal video segmentation algorithm may be useful for MPEG-4 system generating the video object plane (VOP) automatically.
ISSN:0923-5965
1879-2677
DOI:10.1016/S0923-5965(00)00036-9