Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
On Performance Characterization and Optimization for Image Retrieval
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Performance evaluation and optimization for content-based image retrieval
Pattern Recognition
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In [1], we proposed a two-stage retrieval framework which makes not only performance characterization but also performance optimization manageable. There, the performance optimization focused on the second stage of the retrieval framework. In this paper, we extend the method to a full two-stage performance characterization and optimization. In our retrieval framework, the user specifies a high-level concept to be searched for, the size of the image region to be covered by the concept (e.g."Search images with 30-50% of sky") and an optimization option (e.g. "maximum recall", "maximum precision" or "joint maximization of precision and recall"). For the detection of each concept such as "sky", a multitude of concept detectors exist that perform differently. In order to reach optimum retrieval performance, the detector best satisfying the user query is selected and the information of the corresponding concept detector is processed and optimized.Besides the optimization procedure itself the paper discusses the generation of multiple detectors per semantic concept. In experiments, the advantage of joint compared to individual optimization of first and second stage is shown.