The classification process of image plays vital role in computer vision and many multi-mediums and dominated image analysis process. The increasing rate of multi-media data increase the utility of content based image classification. Content based image classification used in different field such as multimedia search in online application, automatic medical diagnose ,video classification and annotation and many more field in current scenario. The performance content based image classification

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... ically depends on lower content of features of image database. now a days in current research trend researchers focus on the high-level semantic investigation of the image content along with the visual content of the image such as colors, textures, and shapes Feature selection and feature optimization play vital role in content based image Classification. The optimized feature of database image improves the searching capacity of content based image classification. In current research trend various method of feature selection and feature optimization are used such as particle of swarm optimization, genetic algorithm and neural network. In this paper proposed a hybrid method for content based image classification, the proposed method is a combination of support vector machine and particle swarm optimization. The extracted feature is optimized with PSO function. In this paper proposed method impalement in MATLAB software and used coral image database. For the empirical evaluation used some standard parameter precision and recall. Our empirical result shows better performance in compression of exiting methods.