Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Naming every individual in news video monologues
Proceedings of the 12th annual ACM international conference on Multimedia
Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Graph Based Approach for Naming Faces in News Photos
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Improving People Search Using Query Expansions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Active learning to maximize accuracy vs. effort in interactive information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Naming People in News Videos with Label Propagation
IEEE MultiMedia
Cross-Media Alignment of Names and Faces
IEEE Transactions on Multimedia
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In this paper, we focus on the problem of automated video annotation. We report on the application of naming faces in soap series by using the weak supervision of narrative texts that describe the events in the video and that are drafted by fans. Several unsupervised methods that operate without any manual labeling of exemplar faces, and methods that use a limited number of labeled exemplars are presented and evaluated. All methods exploit the multiple co-occurrences between faces shown in the video and names mentioned in the texts to compute the strength of the linking and reinforce this coupling by means of an Expectation Maximization algorithm. We show that the unsupervised methods attain competitive results without any prior human effort. The results show F1 values between 80% and 86% for the recognition of the face-name pairs without any human supervision. These figures rise only slightly when a number of faces were manually labeled beforehand. The study gives insights in the benefits and bottlenecks of the proposed approaches, and an error analysis results in guidelines for the choice of a certain technique.