Content Based Image Retrieval

Content Based Image Retrieval (CBIR) is an important issue in multimedia information processing. As the most direct and vivid content, retrieval of image and video information is an important aspect in multimedia information processing. In this thesis, firstly a brief review of the theory, applications and development of CBIR are presented, after which, the techniques and the current state of image retrieval, especially the state of CBIR are summarized. Finally, some important techniques in Content Based Image Retrieval System (CBIRS) are introduced. Texture is one of the most important features of an image. Almost all of the presented CBIR systems use texture as an essential feature for matching and retrieval. In this thesis, a method utilizing both texture features and image center is proposed for retrieval. Experimental results on image database of real flowers and plants show that this method out-performs the method only with texture features based on the co-occurrence matrix. At the same time, CBIR techniques in the compressed domain have attracted much interest. A new fractal coding based indexing technique with histogram of collage errors as the retrieval keys is proposed. Collage error is a quantitative measure of the similarity between the range block and the "best-matched"domain block. Meanwhile, histograms can capture statistical characteristics and can be easily computed. So the proposed method can not only reduce computational complexities greatly, but also enhance the retrieval accuracy. Experimental results on a database of more than 200 texture images illustrate that the proposed method performs excellently.