The LIMSI Broadcast News transcription system
Speech Communication - Special issue on automatic transcription of broadcast news data
Greedy approximation algorithms for finding dense components in a graph
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Name-It: Association of Face and Name in Video
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Character identification in feature-length films using global face-name matching
IEEE Transactions on Multimedia
Role-based identity recognition for TV broadcasts
Multimedia Tools and Applications
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We propose a graph based method to improve the performance of person queries in large news video collections. The method benefits from the multi-modal structure of videos and integrates text and face information. Using the idea that a person appears more frequently when his/her name is mentioned, we first use the speech transcript text to limit our search space for a query name. Then, we construct a similarity graph with nodes corresponding to all of the faces in the search space, and the edges corresponding to similarity of the faces. With the assumption that the images of the query name will be more similar to each other than to other images, the problem is then transformed into finding the densest component in the graph corresponding to the images of the query name. The same graph algorithm is applied for detecting and removing the faces of the anchorpeople in an unsupervised way. The experiments are conducted on 229 news videos provided by NIST for TRECVID 2004. The results show that proposed method outperforms the text only based methods and provides cues for recognition of faces on the large scale.