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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The CLEF 2005 cross–language image retrieval track
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Integrating textual and visual information for cross-language image retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
From text to image: generating visual query for image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Cross-media feedback strategies: merging text and image information to improve image retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
The CLEF 2005 Automatic Medical Image Annotation Task
International Journal of Computer Vision
The CLEF 2005 cross–language image retrieval track
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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This paper regards images with captions as a cross-media parallel corpus, and presents a corpus-based relevance feedback approach to combine the results of visual and textual runs. Experimental results show that this approach performs well. Comparing with the mean average precision (MAP) of the initial visual retrieval, the MAP is increased from 8.29% to 34.25% after relevance feedback from cross-media parallel corpus. The MAP of cross-lingual image retrieval is increased from 23.99% to 39.77% if combining the results of textual run and visual run with relevance feedback. Besides, the monolingual experiments also show the consistent effects of this approach. The MAP of monolingual retrieval is improved from 39.52% to 50.53% when merging the results of the text and image queries.