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Efficient query modification for image retrieval

Content-based Image Retrieval (CBIR) involves retrieving images similar to an example query image in terms of some features extracted from the image. However, inherent subjectivity in user perception of an image results in retrieved images that are largely irrelevant to the user. We propose a novel methodology for efficient understanding of user perception from the query image itself. Our system automatically generates a set of modified images, after the user selects object(s) of interest from the segmented query image. Our goal is to learn the retrieval parameters by modifying the segment-level description of the query image. Segment-level description includes individual segment properties as well as the inter-segment relationships. The user perception is then learnt on the basis of user feedback on this set of modified images. We demonstrate the feasibility and advantages of the proposed approach with examples. The proposed methodology of intra-query learning saves the cost of repeated database search incurred in existing relevance feedback based approaches.