Research Of Content Based Image Retrieval Using Principal Component Analysis

Content-based image retrieval (CBIR) has been an active research area in recent years. The use of digital image brings all kind of image database. The image retrieving from database tables and the management of image database has become a urgent research area. The task of this research is to index, retrieve, administer images automatically and intelligently. On this basis, the user of CBIR system can retrieve images easily, quickly and accurately. And the manger of CBIR system can mange the system without a lot of tedious manual work.First, a full automatic classification of raw images into textured and non-textured images is presented for large-scale image databases. The algorithm uses region segmentation and statistical testing. As a result, textured and non-textured image will be processed in different ways on image retrieval.Second in similarity measurements mentioned above, the paper analyses the drawback of bin-by-bin-form distance and cross-bin-form distance. The weight distance is presented which matches human vision as well as possible. Also, the principle of the distance is based on K-Mean solution. The experimental tests the distance is efficient in image retrieval.In the end, the further research direction is pointed out.