A Region-based Image Retrieval Method

With the development of the technology of Internet and computer vision, the volume of digital image archives is growing as an unbelievable speed. The very large repository of digital information raises challenging problems in retrieval and various other information manipulation tasks. Content-Based Image Retrieval (CBIR) is aimed at efficient retrieval of relevant images from large image database based on automatically derived imagery features. These features are typically extracted from shape, texture, or color properties of query image and image in the database. The relevance between a query image and any target image is ranked according to a similarity measure computed from the features.A region-based image retrieval system segments images into regions or objects and retrieves images based on the similarity between regions. This is consistent to human recognition. In addition, applying fuzzy processing techniques to CBIR has been extensively investigated in the literature. To improve the robustness of a region-based image retrieval system against segmentation related uncertainties,"A Region-based Fuzzy Feature Approach to Content-based Image Retrieval"is selected as the theme of this paper.In the paper, we develop a prototype system started with the accuracy and real time of the content-based image retrieval. We introduce a region-based image retrieval algorithm based on fuzzy logic theory. Every core technology applied in the algorithm is analyzed in detail, including image segmentation, fuzzy feature extracted and fuzzy feature match scheme. We propose a new weight scheme combined area percentage scheme and border favored scheme to provide an overall image-to-image and intuitive similarity quantification. In addition, we do some special operations to shape feature match since we cannot have excellent shape representation.