In this paper, processing of huge number of images is achieved to retrieve a queried image using MapReduce paradigm with different modes. These systems are useful in cases where the traditional single computer cannot process such huge image data. Nevertheless, such processing with a single computer system will take a long time to complete the processing. A total of six types of modes for processing the image data is proposed in this paper. To show the performance of the systems, the results are shown with different number of workers involved in processing the image data. The results show that the proposed MapReduce paradigm with different modes are performing as expected when there is a change in the number of workers involved in processing i.e., the time taken to complete the job is indirectly proportional to the number of workers considered. Even though the time to complete the task has changed, the performance measures: Precision, Recall, F-Measure, Retrieval Rank and Minimum Retrieval Epoch are same for all modes. The computational time for two image datasets: Corel1K and VisTex for a total of five image retrieval methods are evaluated. For completing all the five image retrieval methods on Corel1K, the time saved is 43%, 45% and 68% respectively for the number of workers as 4vs2, 2vs1 and 4vs1 workers. Similarly for VisTex it is 42%, 46% and 68%. The algorithm used for getting the features from the image are the authors recently published algorithms.