  The objective of Content-Based Image Retrieval (CBIR) methods is essentially
to extract, from large (image) databases, a specified number of images similar
in visual and semantic content to a so-called query image. To bridge the
semantic gap that exists between the representation of an image by low-level
features (namely, colour, shape, texture) and its high-level semantic content
as perceived by humans, CBIR systems typically make use of the relevance
feedback (RF) mechanism. RF iteratively incorporates user-given inputs
regarding the relevance of retrieved images, to improve retrieval efficiency.
One approach is to vary the weights of the features dynamically via feature
reweighting. In this work, an attempt has been made to improve retrieval
accuracy by enhancing a CBIR system based on color features alone, through
implicit incorporation of shape information obtained through prior segmentation
of the images. Novel schemes for feature reweighting as well as for
initialization of the relevant set for improved relevance feedback, have also
been proposed for boosting performance of RF- based CBIR. At the same time, new
measures for evaluation of retrieval accuracy have been suggested, to overcome
the limitations of existing measures in the RF context. Results of extensive
experiments have been presented to illustrate the effectiveness of the proposed
approaches.
