  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 on
Radon barcodes. Our method (Single Projection Radon Barcode, or SP-RBC) uses
only a few Radon single projections for each image as global features that can
serve as a basis for weak learners. This is our most important contribution in
this work, which improves the results of the RBC considerably. As a matter of
fact, only one projection of an image, as short as a single SURF feature
vector, can already achieve acceptable results. Nevertheless, using multiple
projections in a long vector will not deliver anticipated improvements. To
exploit the information inherent in each projection, our method uses the
outcome of each projection separately and then applies more precise local
search on the small subset of retrieved images. We have tested our method using
IRMA 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 comparison
with other non-learning methods.
