Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mining Visual Knowledge for Multi-Lingual Image Retrieval
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
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
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In the photo retrieval task of ImageCLEF 2008, we examined the influence of image representations, clustering methods, and query types in enhancing result diversity. Two types of visual concept vectors and hierarchical and partitioning clustering as post-retrieval clustering methods were compared. We used the title fields in the search topics, and either only the title field or both the title and description fields of the annotations were in English. The experimental results showed that one type of visual concept representation dominated the other except under one condition. Also, it was found that hierarchical clustering can enhance instance recall while preserving the precision when the threshold parameters are appropriately set. In contrast, partitioning clustering degraded the results. We also categorized the queries into geographical and non-geographical, and found that the geographical queries are relatively easy in terms of the precision of retrieval results and post-retrieval clustering also works better for them.