Boosting Clustering by Active Constraint Selection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Mining weakly labeled web facial images for search-based face annotation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Automatic Face Annotation in News Images by Mining the Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Retrieval-based face annotation by weak label regularized local coordinate coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Improving constrained clustering with active query selection
Pattern Recognition
Lightweight automatic face annotation in media pages
Proceedings of the 21st international conference on World Wide Web
A unified learning framework for auto face annotation by mining web facial images
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to name faces: a multimodal learning scheme for search-based face annotation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Automatic name-face alignment to enable cross-media news retrieval
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Searching for images of people is an essential task for image and video search engines. However, current search engines have limited capabilities for this task since they rely on text associated with images and video, and such text is likely to return many irrelevant results. We propose a method for retrieving relevant faces of one person by learning the visual consistency among results retrieved from text correlation-based search engines. The method consists of two steps. In the first step, each candidate face obtained from a text-based search engine is ranked with a score that measures the distribution of visual similarities among the faces. Faces that are possibly very relevant or irrelevant are ranked at the top or bottom of the list, respectively. The second step improves this ranking by treating this problem as a classification problem in which input faces are classified as ’person-X’ or ’non-person-X’; and the faces are re-ranked according to their relevant score inferred from the classifier’s probability output. To train this classifier, we use a bagging-based framework to combine results from multiple weak classifiers trained using different subsets. These training subsets are extracted and labeled automatically from the rank list produced from the classifier trained from the previous step. In this way, the accuracy of the ranked list increases after a number of iterations. Experimental results on various face sets retrieved from captions of news photos show that the retrieval performance improved after each iteration, with the final performance being higher than those of the existing algorithms.