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- W2020671194 abstract "In this paper, we propose an optimized higher order conditional random field (CRF) labeling approach toward automated video object segmentation. Our approach introduces a computerized optimization scheme to fine tune the CRF-associated parameters, and hence make the labeling of segmented regions optimal in formulating the video objects. In comparison with the existing efforts using CRF, our optimized CRF has introduced a number of novel features, which can be highlighted as: 1) higher order CRF labeling is made adaptive to video content changes via a windowed dynamics; 2) fusion of multiple features is automatically optimized via fuzzy modeling of incoming video content and regression of parameters; 3) unary potential of higher order CRF labeling is modulated by the shortest path between neighboring regions to improve the effectiveness of higher order CRF labeling; and 4) making the algorithm affordable for a simpler graph-based video segmentation to reduce the overall computing cost, making the proposed algorithm more efficient without compromising on its performances." @default.
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- W2020671194 date "2016-03-01" @default.
- W2020671194 modified "2023-09-24" @default.
- W2020671194 title "An Optimized Higher Order CRF for Automated Labeling and Segmentation of Video Objects" @default.
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- W2020671194 doi "https://doi.org/10.1109/tcsvt.2015.2416557" @default.
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