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Research Article Open Access
Now a day, digital world increase with bandwidth, handheld devices, storage technologies and social networking sites and huge volume of images are stored on web. With significantly huge image database it is difficult to mine that data and retrieve relevant images. Feature Based Image Retrieval is a very important research area in the field of image processing. It is comprises of low level feature extraction such as color, texture and shape and similarity measures for the comparison of images. Recently, the research focus in FBIR has been in reducing the semantic gap, between the low level visual features and the high level image semantics. Here in this paper we have provided comparative study of various methods which are available for each step of FBIR system. In Proposed system architecture HSV space histogram will be used for colour information extraction. Gabor filter will be use for texture feature extraction and shape feature will be extract using moment invariant method. In this study multiple feature extraction will be use by combining above three methods. Based on the extracted feature Support vector machine classification technique is applied. Here classification reduce the search space and reduce retrieval time. After that for given relevant images relevance feedback algorithm is applied which provide user intension for resultant images to the system .This increase classification accuracy by taking feedback from user which decrease semantic gap.