TELECOMParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement

In this paper we describe the participation of TELECOM ParisTech in the ImageClefphoto 2008 challenge. This edition focuses on promoting diversity in the results produced by the retrieval systems. Given the high level semantic content of the topics, search engines based solely on text or visual descriptors are unlikely to offer sa tisfactory results. Our system uses several text and visual descriptors, as well as several comb ination algorithms to improve the overall retrieval performance. The text part includes a col lection of manually built boolean queries and a set of textual descriptors extracted automati cally using dictionary filtering and dimensionality reduction. Text and visual descriptors are combined using two strategies: adhoc concatenation and re-ranking. Diversity makes it possible to reduce the redundancy in the final results and it is obtained using two techniques, thr eshold clustering and maxmin exploration. Several runs were submitted to the challenge, including individual (text or visual), combined, and with different settings of diversity. The results show that the combined runs outperform by a significant amount the individual runs. Thes e results clearly corroborate (i) the complementarity of text and visual descriptors and (ii) the effectiveness of boolean queries suggesting promising future research directions.

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