Research On Some Technologies Of Local Feature Image Retrieval

Content-based image retrieval (CBIR) is a kind of new technique, which applies image low-level information and currently existing processing methods to extract image feature and match between images. Currently most widely used CBIR methods are based on global image information. However, for practical application users are more focus on certain semantic features of an image. In order to achieve rational results, these methods based on image segmentation and auto regional feature extraction are introduced into some image retrieval systems. Unfortunately, so far there is no a general method and a segmentation quality standard. It is obvious that the human perception for vision has subjectivity, because this kind of subjectivity the effectiveness of image retrieval is affected. In order to address the above problems, this paper mainly is devoted to explore such a kind of image retrieval methods based on local feature. In this article, two kinds of new methods are proposed, i.e., an image retrieval method composited of color and shape features, an image retrieval method based on adaptive detecting and extracting an object of interesting (ROI), and an image retrieval engine system based on web has also been developed.The rest of this article is organized as follows:An image retrieval method compositing features of color and object contour curve is presented. Firstly, an image is segmented into multi-clusters. Secondly, an interesting object in image is extracted. Furthermore, its contour is extracted. Thirdly, the contour is transformed by affine, and processed by the minimum. The contour contains the whole information of interesting object and preserves geometric invariance. In addition, a histogram for primary cluster with color feature is extracted. Such an extracted histogram contains not only color information but also spatial location information. Finally, a weighted average for color distance histogram and distance deviation of contour curve is applied as similarity measure to match between two images. Experimental results show that the proposed method achieves a better retrieval precision.Another method, which is called a novel image retrieval method based on region of interest (ROI), combines multiple features with mean shift (MFMS) tracking algorithm and EM scale transformation (EMST). For typical mean shift (MS) tracking method only color histogram is considered, other features, such as spatial distribution and texture feature, are neglected. Hence, it is easy to fall failure during detection ROI. However, in our proposed method spatial distribution is integrated into MS, therefore, intuitively for detecting ROI MFMS is of a more fine effectiveness. In fact, experimental results also show that MFMS is able to detect the position of ROI more accurately and robustly than one of MS. In addition, the EMST uses EM-like algorithm to estimate the local position and covariance matrix, which describes the approximate scale of ROI. It is better to quickly and accurately describe such a scale change in the process of image retrieval.An image retrieval engine based on web has also been implemented, which is a kind of software package based on CBIR and B/S architecture. With respect to the different user, this software has implemented four different image retrieval functions. A primary clustering matching method, whose advantage is to use multi-features, is proposed by our research team.