Individualization of Content-Based Image Retrieval Systems via Objective Feature Subset Selection

We explore the use of objective features to model the subjective perception of similarity between two images that have been extracted from an image database. We present a Content-based Image Retrieval system which evolves and uses different image similarity measures for different users. Specifically, a user-supplied relevance feedback procedure allows the system to determine which subset of a set of objective features approximates more efficiently the subjective image similarity of a specific user. Our implementation and evaluation of the system verifies our hypothesis and exhibits significant improvement in perceived image similarity.

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