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Using content-based binary codes to tag digital images has emerged as apromising retrieval technology. Recently, Radon barcodes (RBCs) have beenintroduced as a new binary descriptor for image search. RBCs are generated bybinarization of Radon projections and by assembling them into a vector, namelythe barcode. A simple local thresholding has been suggested for binarization.In this paper, we put forward the idea of "autoencoded Radon barcodes". Usingimages in a training dataset, we autoencode Radon projections to performbinarization on outputs of hidden layers. We employed the mini-batch stochasticgradient descent approach for the training. Each hidden layer of theautoencoder can produce a barcode using a threshold determined based on therange of the logistic function used. The compressing capability of autoencodersapparently reduces the redundancies inherent in Radon projections leading tomore accurate retrieval results. The IRMA dataset with 14,410 x-ray images isused to validate the performance of the proposed method. The experimentalresults, containing comparison with RBCs, SURF and BRISK, show that autoencodedRadon barcode (ARBC) has the capacity to capture important information and tolearn richer representations resulting in lower retrieval errors for imageretrieval measured with the accuracy of the first hit only.

Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform