# Similarity Searching in Large Image Databases

 dc.contributor.author Petrakis, Euripides G.M. en_US dc.contributor.author Faloutsos, Christos en_US dc.date.accessioned 2004-05-31T22:29:19Z dc.date.available 2004-05-31T22:29:19Z dc.date.created 1994-12 en_US dc.date.issued 1998-10-15 en_US dc.identifier.uri http://hdl.handle.net/1903/682 dc.description.abstract We propose a method to handle approximate searching by image content in large image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of labeled'' or expected'' objects (e.g., heart'', lungs'' etc.) are common in all images of a given application domain in addition to a variable number of unexpected'' or unlabeled'' objects (e.g., tumor'', hematoma'' etc.). The method can answer queries by example such as {\em find all X-rays that are similar to Smith's X-ray}''. The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate so that all images up to a pre-specified degree of similarity (tolerance) are retrieved, (b) it has no false dismissals'' (i.e., all images qualifying query selection criteria are retrieved) and (c) it scales-up well as the database grows. We implemented the method and ran experiments on a database of synthetic (but realistic) medical images. The experiments showed that our method significantly outperforms sequential scanning by up to an order of magnitude. (Also cross-referenced as UMIACS-TR-94-134) en_US dc.format.extent 495766 bytes dc.format.mimetype application/postscript dc.language.iso en_US dc.relation.ispartofseries UM Computer Science Department; CS-TR-3388 en_US dc.relation.ispartofseries UMIACS; UMIACS-TR-94-134 en_US dc.title Similarity Searching in Large Image Databases en_US dc.type Technical Report en_US dc.relation.isAvailableAt Digital Repository at the University of Maryland en_US dc.relation.isAvailableAt University of Maryland (College Park, Md.) en_US dc.relation.isAvailableAt Tech Reports in Computer Science and Engineering en_US dc.relation.isAvailableAt UMIACS Technical Reports en_US
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