Image retrieval (IR) generally is known as a collection of techniques for retrieving images on the basis of features, either low-level (content-based IR) or high-level (semantic-based IR). Since semantic-based features rely on low-level ones, in this chapter the reader initially is familiarized with the most widely used low-level features. An efficient way to present these features is by means of a statistical tool that is capable of bearing concrete information, such as the histogram. For use in IR, the histograms extracted from the previously mentioned features need to be compared by means of a metric. The most popular methods and distances are, thus, apposed. Finally, a number of IR systems using histograms are presented in a thorough manner, and their experimental results are discussed. The steps in order to develop a custom IR system along with modern techniques in image feature extraction also are presented. Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission

Recent years have envisaged a sudden increase in the use of multimedia content like images and videos. This increase has created the problem of locating desired digital content from a very large multimedia database. This paper presents an optimized Content Based Image Retrieval (CBIR) system that uses multiple feature fusion and matching to retrieve images from a image database. Three features, namely, color, texture and shape are used. A modified color histogram is used to extract color features, the standard DWT method was combined with Rotated Wavelet Filter (RWF) features and dual tree complex wavelet transform (DT-CWT) are combined to select texture features and active contour model is used to select the shape features. K-means and SOM algorithms are used for clustering and dimensional reduction. The similarity measure used combines spatial distance, direction distance and Euclidean distance during matching process. Experimental results prove that