  The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query
by Image Content (QBIC), is to help users to retrieve relevant images based on
their contents. CBIR technologies provide a method to find images in large
databases by using unique descriptors from a trained image. The image
descriptors include texture, color, intensity and shape of the object inside an
image. Several feature-extraction techniques viz., Average RGB, Color Moments,
Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric
Moment have been critically compared in this paper. However, individually these
techniques result in poor performance. So, combinations of these techniques
have also been evaluated and results for the most efficient combination of
techniques have been presented and optimized for each class of image query. We
also propose an improvement in image retrieval performance by introducing the
idea of Query modification through image cropping. It enables the user to
identify a region of interest and modify the initial query to refine and
personalize the image retrieval results.
