In this thesis, a Content-Based Image Retrieval (CBIR) system that is based onrnsegmentation and color feature is presented. CBIR helps to organize and retrieve digitalrnimages using their visual content from large databases. The CBIR system proposed in thisrnwork consists of segmentation, feature extraction and similarity measuring techniques tornstore and to retrieve the images.rnThe segmentation is done using the K-Means clustering algorithm. Unlike existingrnsystems, the segmentation used is unreliable that the image is divided into many smallrnregions that are far from representing semantic objects.rnFeature extraction is done for each region of the image. A region is represented by itsrnaverage color and number of pixels. The average color is calculated for each channel ofrnthe RGB color space. An image is stored in the database as a collection of regions.rnThe similarity of two images is based on the similarity of their regions. Two differentrnsimilarity measures are proposed in this work. One is based on count of similar regionsrnand the other measure is based on the sum of pixels of the similar regions. Regionrnsimilarity is based on the Euclidean distance between their average colors and the numberrnof pixels of the regions. Two images that have more number of similar regions are said tornbe more similar.rnThe proposed system is compared with some existing systems like Earth Moverâ€™srnDistance (EMD) and SIMPLIcity using three parameters: precision, average rank andrnstandard deviation of the ranks. The performance of the proposed system is promisingrnand found to be better than EMD. However, the system didnâ€™t perform well compared tornSIMPLIcity, owing to the fact that SIMPLIcity uses color, shape and texture for thernfeature extraction technique whereas the proposed system uses only the color feature.