Gradual transition detection with conditional random fields

Jinhui Yuan, Jianmin Li, Bo Zhang

Abstract: In this paper, we view gradual transition detection as a sequence labeling problem and propose to use Conditional Random Fields (CRFs) for this purpose. CRFs is a state-of-the-art sequence labeling approach. It provides a unified way to integrate various useful clues to form a decision system. Moreover, it has principled way for parameter estimation and inference. Compared to rule-based approaches, gradual transition detection with CRFs requires fewer human interactions while designing the system. The experiments on TRECVID platform show that CRFs can achieve comparable performance to that of the state-of-the-art approaches.