Content-based sub-image retrieval using relevance feedback

Abstract

This paper presents the use of relevance feedback to the problem of content-based sub-image retrieval (CBsIR). Relevance feedback is used to improve the accuracy of successive retrievals via a tile re-weighting scheme that assigns penalties to each tile of database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant (positive) and irrelevant (negative) images identified by the user. Performance evaluation on a dataset of over 10,000 images shows the effectiveness and efficiency of the proposed framework. Using 64 quantized colors in the RGB color space, the system can achieve a stable average recall value of 70% within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds.