Image Filtering with MapReduce in Pseudo-Distribution Mode

Abstract

The massive volume of video and image data, compels them to be stored in a distributed file system. To process the data stored in the distributed file system, Google proposed a programming model named MapReduce. Existing methods of processing images held in such a distributed file system, requires whole image or a substantial portion of the image to be streamed every time a filter is applied. In this work an image filtering technique using MapReduce programming model is proposed, which only requires the image to be streamed only once. The proposed technique extends for a cascade of image filters with the constrain of a fixed kernel size. To verify the proposed technique for a single filter a median filter is applied on an image with salt and pepper noise. In addition a corner detection algorithm is implemented with the use of a filter cascade. Comparison of the results of noise filtering and corner detection with the corresponding CPU version show the accuracy of the method.