New Feature Vector for Image Retrieval: Sum of Value of Histogram Bins

The large amount of image collections available from a variety of sources have posed increasing technical challenges to computer systems to store/transmit and index/manage the image data to make such collections easily accessible. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval (CBIR) system. CBIR extracts the features of query image and try to match them with the extracted features of images in the database. Then based on the similarity measures and threshold the best possible candidate matches are given as result. This paper describe a novel & effective approach to content based image retrieval that represent each image in database by a vector of feature values called ¿New Feature Vector for Image Retrieval :Sum of Value of Histogram Bins¿. Proposed method is a simple and new which can be easily implemented in a programming language. In this technique sum of value of equalized histogram bins used as a feature vector of image. This classifier is well suitable for features extracted and fast in computation for CBIR systems. Simple Euclidean Distance used to compute the similarity measures of images for Content Based Image Retrieval application. This technique gives acceptable results in a simple and fast way.