Survey on Biomedical Image Retrieval System
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 7
Abstract
With the increasing use of Social media thousands of images are being downloaded & uploaded every day. Contentbased image retrieval (CBIR) was in research for the last few years. In particular, there has been growing interest in indexing biomedical images by content. Manual indexing of images for content-based retrieval is cumbersome, error prone, and prohibitively expensive. Due to the lack of effective automated methods, however, biomedical images are typically annotated manually and retrieved using a text keyword-based search. A common drawback of such systems is that the annotations are imprecise with reference to image feature locations, and text is often insufficient in enabling efficient image retrieval. Even such retrieval is impossible for collections of images that have not been annotated or indexed. In the support, the probabilistic outputs of the support vector machine (SVM) classifier as category prediction of query and database images are exploited at first to filter out irrelevant images, thereby reducing the search space for similarity matching.
Authors and Affiliations
Jyoti Patil, Surayya Shaikh
Keywords
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