Region Of Interest Based Image Retrieval

Region-based image retrieval (RBIR) is an important research aspect of CBIR. Region-based retrieval applies image segmentation to decompose an image into several regions, and uses region visual features to represent and index the image. It can reduce the gap between low-level feature and high-level semantic features and more close to the human perception. These regions not only include relevant objects, but also irrelevant image areas. The irrelevant areas limit the effectiveness of system. To overcome this limitation, the systems must be able to determine similarity based on relevant regions alone. It is called region of interest (ROI).The paper analyzes and summarizes the fundamental, key techniques and performance evaluation of CBIR. An image segmentation method using image color and mathematical morphology is developed in the thesis. It divide image into several regions, then, combinate these regions based on mathematical morphology in order to reduce over-segmentation. Some regions in level of object are shaped.In the research on Zernike moment character, 21 moments are extracted from regions of divided image. These moments combine with the features of color, center, size, contour, moments to represent and index the image. In order to improve the efficiency of retrieval, step match is adopted through ROI-based image retrieval.Relevance feedback is one of important study aspects. It is first introduced in document retrieval, in order to fill the blank between low-level features and high-level semantic features in CBIR, relevance feedback is adopted by CBIR. In the view of pattern classification and machine learning, a classifier is constructed by support vector machine (SVM) for relevance feedback. Users can label the right results and wrong results after one query. Right set and wrong set are formed by these labeled results, which are used for constructing SVM classifier model. Next retrieval is based on this model for finding more relevant images efficiently. It is showed that it has better generalization ability in small sample problem.A prototyping system of CBIR is built in the thesis which can be used for CBIR based on low level visual character, for example, histogram, color pair, Tamura texture, moment invariants, Hu moment feature, and the Zernike moment feature that is adopted in the thesis for ROI based image retrieval.