Using Color And Shape Features For Image Retrieval

With the rapid development of digital image process technique and popularization of INTERNET, Digital images are widely used in many respects. A problem appears that how to locate a desired image in a large and varied database efficiently. So the Content-Based Image Retrieval (CBIR) technique is proposed, which is widely discussed in literature and becomes a new hotspot. The method for feature extraction is the key issue in this field. In this paper, we mainly discuss algorithms for image retrieval based on color and shape features.Color histogram is widely used in CBIR. However, the classical color histogram method has some drawbacks, such as without containing information about spatial locations or distributions of pixels in an image. To overcome the limitations above, a meaningful regions-based algorithm for color image retrieval is proposed: the color cluster parameter of images are first calculated, those images of high value parameters are segmented into sub-regions, and the others are only divided into sub-blocks ,each of which is regarded as a region. Each region is given a dynamic weight, and the total similarity between two images is computed according to those regions'color histograms.In this paper, we implement and improve an algorithm based on Fourier radius descriptor. An image is segmented and transformed into binary color, and the profile is tracked. The distance of each sample point from the region center is determined, and these distance values form a list, on which a Fourier transform is carried through. The result of transform can be used to describe the shape feature and support retrieval.We propose the framework of DMIR (DM Image Retrieval) system, with optimization of the background DBMS.DMIR adopt the Optimized Pyramid Technique (OPT) to deal with the high-dimensional data spaces so that it can obtain results as quickly as possible. On this foundation, the DMIR system and some retrieval algorithms are implemented. The results of our experiments comparing with other algorithms demonstrate that the methods proposed above are of great value in practice.