Rich representation and ranking for photographic image retrieval in ImageCLEF 2007

Abstract : The task of ad-hoc photographic image retrieval in ImageCLEF 2007 international benchmark is to retrieve relevant images in the database to the user query formulated as keywords and image examples. This paper presents rich representation and indexing technologies exploited in our system that participated in ImageCLEF 2007. It uses diverse visual content representation, text representation, pseudo-relevance feedback and fusion, which make our system, with mean average precision 0.2833, in the 4th place among 457 automatic runs submitted from 20 participants to photographic ImageCLEF 2007 and in the 2nd place in terms of participants. Our systematic analysis in the paper demonstrates that 1) combing diverse low-level visual features and ranking technologies significantly improves the content-based image retrieval (CBIR) system; 2) cross-modality pseudo-relevance feedback improves the system performance; and 3) fusion of CBIR and TBIR outperforms individual modality based system.
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Communication dans un congrès
IEEE 10th Workshop on Multimedia Signal Processing, 2008, 2008, Unknown, pp.553--557, 2008
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https://hal.inria.fr/hal-00953866
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 16:02:43
Dernière modification le : mardi 24 avril 2018 - 13:29:33

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  • HAL Id : hal-00953866, version 1

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Sheng Gao, Jean-Pierre Chevallet, Joo-Hwee Lim. Rich representation and ranking for photographic image retrieval in ImageCLEF 2007. IEEE 10th Workshop on Multimedia Signal Processing, 2008, 2008, Unknown, pp.553--557, 2008. 〈hal-00953866〉

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