Abstract
Content Based Image Retrieval (CBIR) is a way of querying image databases. CBIR looks at visual properties of an image as “search terms” and returns pictures from a database that share the same or almost similar visual properties. Most CBIR systems in the literature works by extracting the image color, texture and shape features before comparing them with those in the database and then compute the distance between features of images for retrieval purposes. In this proposed work, we use a MapReduce model framework to index the large-scale images and Spark has been used as a proportionate method of retrieving the index, which runs on the higher layer of MapReduce and Hadoop distributed file system (HDFS) environment. HDFS provides an in-memory data storage and fast retrieval mechanism using the indexing process. The image retrieval is performed in alignment with the K-Nearest Neighbour’s model using Apache implementation. The processing time has been evaluated with the Hadoop framework in CBIR. The proposed approach takes 10% less time to index images than the distributed image segmentation method discussed in the literature.
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