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This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content.
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The retrieval of similar patient cases based on image content profits from the predominant role of images in the radiology domain.
Fig. 3 Proposed framework for image retrieval based on individual image representations.
With the rapid proliferation of large-scale web images, recent years have witnessed more and more images labeled with user-provided tags, which leads to considerable effort made on hashing based image retrieval in huge databases.
In content based image retrieval (CBIR) system, search engine retrieves the images similar to the query image according to a similarity measure.
It can be used for summarizing personal photo album, guiding multiple images' editing, boosting unsupervised object recognition, improving content based image retrieval and so on.
The proposed architecture addresses the issues of keyword based image retrieval and content-based image retrieval through the use of qualitative spatial representations over semantic image annotations.
The purpose of content based image retrieval (CBIR) systems is to allow users to retrieve pictures from large image repositories.
Retrieval efficiency and accuracy are the important issues in designing content based image retrieval system.
A new image indexing and retrieval system for content based image retrieval (CBIR) is proposed in this paper.
Content Based Image Retrieval (CBIR) is concerned with the retrieval of images similar to a specified image, from an image repository.
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