Compressed domain video saliency detection using global and local spatiotemporal features
- 주제(키워드) Compressed domain , Image analysis , Image understanding , Motion analysis , Partial decoding , Spatiotemporal feature , Video saliency detection , Visual attention
- 등재 SCIE, SCOPUS
- 발행기관 Academic Press Inc.
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000123069
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.jvcir.2015.12.011
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
A compressed domain video saliency detection algorithm, which employs global and local spatiotemporal (GLST) features, is proposed in this work. We first conduct partial decoding of a compressed video bitstream to obtain motion vectors and DCT coefficients, from which GLST features are extracted. More specifically, we extract the spatial features of rarity, compactness, and center prior from DC coefficients by investigating the global color distribution in a frame. We also extract the spatial feature of texture contrast from AC coefficients to identify regions, whose local textures are distinct from those of neighboring regions. Moreover, we use the temporal features of motion intensity and motion contrast to detect visually important motions. Then, we generate spatial and temporal saliency maps, respectively, by linearly combining the spatial features and the temporal features. Finally, we fuse the two saliency maps into a spatiotemporal saliency map adaptively by comparing the robustness of the spatial features with that of the temporal features. Experimental results demonstrate that the proposed algorithm provides excellent saliency detection performance, while requiring low complexity and thus performing the detection in real-time. © 2015 Elsevier Inc. All rights reserved.
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