The Journal of Machine Learning Research
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
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 large scale system for searching and browsing images from the world wide web
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Impact of visual information on text and content based image retrieval
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Multimodal knowledge-based analysis in multimedia event detection
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
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In this paper, we study how to automatically exploit visual concepts in a text-based image retrieval task. First, we use Forest of Fuzzy Decision Trees (FFDTs) to automatically annotate images with visual concepts. Second, using optionally WordNet, we match visual concepts and textual query. Finally, we filter the text-based image retrieval result list using the FFDTs. This study is performed in the context of two tasks of the CLEF2008 international campaign: the Visual Concept Detection Task (VCDT) (17 visual concepts) and the photographic retrieval task (ImageCLEFphoto) (39 queries and 20k images). Our best VCDT run is the 4th best of the 53 submitted runs. The ImageCLEFphoto results show that there is a clear improvement, in terms of precision at 20, when using the visual concepts explicitly appearing in the query.