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The idea of Radon barcodes (RBC) has been introduced recently. In this paper,we propose a content-based image retrieval approach for big datasets based onRadon barcodes. Our method (Single Projection Radon Barcode, or SP-RBC) usesonly a few Radon single projections for each image as global features that canserve as a basis for weak learners. This is our most important contribution inthis work, which improves the results of the RBC considerably. As a matter offact, only one projection of an image, as short as a single SURF featurevector, can already achieve acceptable results. Nevertheless, using multipleprojections in a long vector will not deliver anticipated improvements. Toexploit the information inherent in each projection, our method uses theoutcome of each projection separately and then applies more precise localsearch on the small subset of retrieved images. We have tested our method usingIRMA 2009 dataset a with 14,400 x-ray images as part of imageCLEF initiative.Our approach leads to a substantial decrease in the error rate in comparisonwith other non-learning methods.

Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections