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Overview
The need for efficient content-based image retrieval has increased tremendously in areas such as biomedicine, military, commerce, education, and Web image classification and searching. In the biomedical domain, content-based image retrieval can be used in patient digital libraries, clinical diagnosis, searching of 2-D electrophoresis gels, and pathology slides.Integrated Region-Based Image Retrieval presents a wavelet-based approach for feature extraction, combined with integrated region matching. An image in the database, or a portion of an image, is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. A measure for the overall similarity between images is developed as a region-matching scheme that integrates properties of all the regions in the images. The advantage of using this "soft matching" is that it makes the metric robust to poor segmentation, an important property that previous research has not solved. Integrated Region-Based Image Retrieval demonstrates an experimental image retrieval system called SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries). This system validates these methods on various image databases, proving that such methods perform much better and much faster than existing ones. The system is exceptionally robust to image alterations such as intensity variation, sharpness variation, intentional distortions, cropping, shifting, and rotation. These features are extremely important to biomedical image databases since visual features in the query image are not exactly the same as the visual features in the images in the database.
Integrated Region-Based Image Retrieval is an excellent reference for researchers in the fields of image retrieval, multimedia, computer vision and image processing.
Synopsis
Content based image retrieval is the set of techniques for retrieving relevant images from an image database on the basis of automatically derived image features. Wang (Pennsylvania State U.) explains the CBIR system, designed by him and his colleagues, that utilizes wavelet- based feature extraction, semantics classification, and integrated region matching. Images are represented by sets of regions, characterized by color, texture, shape, and location and are then classified into semantic categories. This enhances semantically-adaptive searching methods by narrowing down the searching range within the database. Wang believes that his methods will have many applications, especially in the biomedical fields.
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