Image Retrieval Using DCT Coefficients of Pixel Distribution and Average Value of Row and Column Vector

Retrieving image from large & varied collections using image content (such as color, shape, texture) as a key is challenging & important problem . This paper describes a two approaches to content based image retrieval (CBIR) that represent each image in database by a vector of feature values called ¿Image Retrieval using DCT coefficients of pixel distribution & average value of row & column vector¿. Here we propose simple approaches that can be easily implemented in a programming language. In this paper we apply DCT on average value of row and column vector and DCT on pixel distribution of row and column vector. Feature vector each image is DCT coefficients. Here DCT coefficients are calculated by using two techniques. These two techniques are compared using precision and recall of each class by using each method. This classifiers is well suitable for features extracted and fast in computation for CBIR systems. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. This technique gives acceptable results in a simple and fast way.