Proceedings of the 6th ACM international conference on Image and video retrieval
Overview of the ImageCLEFphoto 2008 photographic retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
The visual concept detection task in ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
A comparative study of diversity methods for hybrid text and image retrieval approaches
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
A comparative study of diversity methods for hybrid text and image retrieval approaches
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
A Learning to Rank framework applied to text-image retrieval
Multimedia Tools and Applications
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In this article, we focus our efforts (i) on the study of how to automatically extract and exploit visual concepts and (ii) on fast visual diversity. First, in the Visual Concept Detection Task (VCDT), we look at the mutual exclusion and implication relations between VCDT concepts in order to improve the automatic image annotation by Forest of Fuzzy Decision Trees (FFDTs). Second, in the ImageCLEFphoto task, we use the FFDTs learnt in VCDT task and WordNet to improve image retrieval. Third, we apply a fast visual diversity method based on space clustering to improve the cluster recall score. This study shows that there is a clear improvement, in terms of precision or cluster recall at 20, when using the visual concepts explicitly appearing in the query and that space clustering can be effciently used to improve cluster recall.