Video Concept Detection


The broad availability of videos has led to a general and strong demand for effective and efficient video retrieval. Concept-based video retrieval is a promising approach. However, its success greatly depends on the accuracy of concept detection. We proposed several frameworks to take advantage of both contextual correlation and temporal dependency to improve accuracy for video concept detection from user-provided annotations and/or detector-generated predictions.


Cross-domain Multi-cue Fusion for Concept-based Video Indexing
Ming-Fang Weng, Yung-Yu Chuang
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), October 2012
Collaborative Video Re-indexing via Matrix Factorization
Ming-Fang Weng, Yung-Yu Chuang
ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), May 2012
Multi-Cue Fusion for Semantic Video Indexing
Ming-Fang Weng, Yung-Yu Chuang
ACM Multimedia 2008
Association and Temporal Rule Mining for Post-Processing of Semantic Concept Detection in Video
Ken-Hao Liu, Ming-Fang Weng, Chi-Tao Tseng, Yung-Yu Chuang, Ming-Syan Chen
IEEE Transactions on Multimedia, Feburary 2008


This research is supported by:

cyy -a-t-