An Content-Based Medical Image Mining System Based On Fuzzy C-Means Associate Oppositional Crow Search Optimization
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Abstract
In recent, Content-Based Image Retrieval (CBIR) requires remained unique on the best research areas in the ground of processor presentations. The advent of the World Wide Web, proliferation of digital cameras and as well as the use of multimedia systems for public and private use, images have become more and more common around the world. The significant objective of this research is to enhance the retrieving performance of the CBIR system by incorporating optimization techniques to predict appropriate centroid in Fuzzy C-means (FCM). The intention to incorporate an optimization technique to predict FCM centroids certainly reduces complexity and computation time. The swarm intelligence method is determined to solve the prediction of optimal FCM centers of gravity and to understand the basic methodology in implementing crow search Optimization (CSO) and particle swarm optimization (PSO) urges the development of an oppositional Crow Search Optimization (OCSO). The results show that the incorporation of OCSO into FCM shows superior results competitive techniques.
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