Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Clustering for photo retrieval at Image CLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Diversity in photo retrieval: overview of the ImageCLEFPhoto task 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
FIRE – flexible image retrieval engine: ImageCLEF 2004 evaluation
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
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For this year's Image CLEF Photo Retrieval task, we investigated the effectiveness of 1) image content-based retrieval, 2) text-based retrieval, and 3) integrated text and image retrieval. We investigated whether the clustering of results can increase diversity by returning as many different clusters of images in the results as possible. Our image system used the FIRE engine to extract image features such as color, texture, and shape from a data collection consisting of about half a million images. The text-retrieval backend used Lucene to extract texts from image annotations, title, and cluster tags. Our results revealed that among the three image features, color yields the highest retrieval precision, followed by shape, then texture. A combination of color extraction with text retrieval increased precision, but only to a certain extent. Clustering also improved diversity, only in our text-based clustering runs.