Parallel Implementation of Texture Based Image Retrieval on The GPU

Abstract

Most image processing algorithms are inherently parallel, so mu ltithreading processors are suitable in such applications. In huge image databases, image processing takes very long time fo r run on a single core processor because of single thread execution of algorith ms . Graphical Processors Units (GPU) is more co mmon in most image p rocessing applications due to multithread execution of algorith ms, programmab ility and low cost. In this paper we implement texture based image retrieval system in parallel using Co mpute Unified Device Architecture (CUDA) programming model to run on GPU. The main goal of this research work is to parallelize the process of texture based image retrieval through entropy, standard deviation, and local range, also whole process is much faster than normal. Our wo rk uses extensive usage of highly mu ltithreaded architecture of mu lt i-cored GPU. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 140.046×over the serial imp lementation. The average Precision and the average Recall of presented method are 39.67% and 55.00% respectively.