Improving Performance in Terms of Computation Time in CBIR Using Hadoop Map-Reduce

Image Processing

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. Content Based Image Retrieval (CBIR) is a technique which uses feature extraction and we extract and execute all the low level feature color features, texture features and shape features and their methods. User need to give a query image and similarity measurement engine which is responsible in estimating the similarity between the query image and database images and then ranking. of images according to similarity to the input query image and reduces computational time for image retrieval by using MapReduce for distributed dataset as well as increases precision of image by combining all three feature extraction techniques. We have compared different techniques as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

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