An Optimized Higher Order CRF for Automated Labeling and Segmentation of Video Objects
Jiangmin Jiang, Xiaonan Song · IEEE Transactions on Circuits and Systems for Video Technology · 2015
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.