Analysis of Color Moment as a Low Level Feature in Improvement of Content Based Image Retrieval

Abstract

In the recent past, the rapid intensification of the Internet has significantly enhanced the quantity of image collections accessible owing to the simplicity with which images are being formed or stored. CBIR (Content Based Image Retrieval) phenomenon is therefore highly encouraged by this requirement of unbeaten and proficient exploration of the large image databases. Consequently, the low level feature extraction of the visual contents of an image and their analysis is very significant in terms of CBIR. These low level features can be colour, texture and shape features. As Colour based image retrieval procedure is the trendiest of all these feature extraction algorithms, hence in this paper the color moments of the Hue, Saturation, and Value (HSV) component images in HSV color space are used as feature extraction algorithm. After the successful calculation of features for extraction, similarity computation is done using Euclidean Distance in between the test image and object images and finally the image retrieval is done. Analysis of this paper shows that the training time required for individual image, as well as, all the images in the database is very small which provides instantaneous retrieval. The estimation of the proposed approach is conceded out using the standard precision, recall and f-score measures, and the experimental results demonstrate that the proposed method has higher accuracy and retrieval rate than the conventional methods.