Automatic partitioning of full-motion video
Multimedia Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection
IEEE Transactions on Circuits and Systems for Video Technology
Detection of hard cuts and gradual transitions from video using fuzzy logic
International Journal of Artificial Intelligence and Soft Computing
Labeling TV stream segments with conditional random fields
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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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.