A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
FIRE in ImageCLEF 2005: combining content-based image retrieval with textual information retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Supervised machine learning based medical image annotation and retrieval in ImageCLEFmed 2005
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Improving a discriminative approach to object recognition using image patches
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
The CLEF 2004 cross-language image retrieval track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Dublin city university at CLEF 2004: experiments with the ImageCLEF st. andrew's collection
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Biomedical image classification with random subwindows and decision trees
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
A refined SVM applied in medical image annotation
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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There have been many features developed for images, like Blob, image patches, Gabor filters, etc. But generally the calculation cost is too high. When facing a large image database, their responding speed can hardly satisfy users’ demand in real time, especially for online users. So we developed a new image feature based on a new region division method of images, and named it as ‘stripe’. As proved by the applications in ImageCLEF’s medical subtasks, stripe is much faster at the calculation speed compared with other features. And its influence to the system performance is also interesting: a little higher than the best result in ImageCLEF 2004 medical retrieval task (Mean Average Precision — MAP: 44.95% vs. 44.69%), which uses Gabor filters; and much better than Blob and low-resolution map in ImageCLEF 2006 medical annotation task (classification correctness rate: 75.5% vs. 58.5% & 75.1%).