Abstract
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.
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Chang, YC., Lin, WC., Chen, HH. (2006). A Corpus-Based Relevance Feedback Approach to Cross-Language Image Retrieval. In: Peters, C., et al. Accessing Multilingual Information Repositories. CLEF 2005. Lecture Notes in Computer Science, vol 4022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11878773_66
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DOI: https://doi.org/10.1007/11878773_66
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