Parallel Implementation of Texture Based Image Retrieval on The GPU

Abstract

Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU) is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement texture based image retrieval system in parallel using Compute 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 work uses extensive usage of highly multithreaded architecture of multi-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 implementation. The average Precision and the average Recall of presented method are 39.67% and 55.00% respectively

Department of IT, GIT, GITAM University, Visakhapatnam

“…VLBP method has been proposed in [19], which is useful for dynamic texture extraction from a video. Texture features are extracted by using three sequential frames of the video as shown in below figure, Fig.…”

“…Ibrahim S. I. Abuhaiba et al [18] have proposed a novel image retrieval method based on global texture and local color features of the image. Hadis Heidari et al [19] have introduced a method for image retrieval based on graphical processors units. K. Prasanthi Jasmine et al in [20] have introduced a new framework meant for image retrieval based on color and wavelet transform features.…”