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. GPU is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we show how to implement the MPRG-7 Edge Histogram Descriptor in parallel using CUDA programming model on a GPU. The Edge Histogram Descriptor describes the distribution of various types of edges with a histogram that can be a tool for image matching. This feature is applied to search images from a database which are similar to a query image. 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 14.74×over the serial implementation. The average precision and the average recall of presented method are 67.02% and 55.00% respectively
