An Optimized Higher Order CRF for Automated Labeling and Segmentation of Video Objects

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.