A Corpus-Based Relevance Feedback Approach to Cross-Language Image Retrieval

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.

• Type: Conference
• Year: 2005
• Where: CLEF
• Authors: Yih-Chen Chang, Wen-Cheng Lin, Hsin-Hsi Chen
• Keywords: CLEF 2005, Cross-media Parallel Corpus, Relevance Feedback, Textual Run