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A Novel Approach for Image Retrieval System Combining Color, Shape & Texture Features

In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content based searching. In this paper a review on the current state of the art in content based image retrieval (CBIR), a technique for retrieving images on the basis of automatically derived features such as color, texture and shape is presented. Our findings are based both on a review of the relevant literature and on discussions with researchers and practitioners in the field. Here we discuss a new content based image retrieval approach based on three well known algorithms Mean Shift Segmentation, Wavelet Transform and PCA. The evaluation of the proposed approach is carried out using the standard precision and recall measures. Experimental results for the Caltech and Corel databases showed the effectiveness of our proposed method to be very promising and performance to be better than other existing approaches.