Authors:
Morteza Babaie
1
;
H. R. Tizhoosh
2
;
Shujin Zhu
3
and
M. E. Shiri
4
Affiliations:
1
University of Waterloo and Amirkabir University of Technology, Canada
;
2
University of Waterloo, Canada
;
3
Nanjing Univ. of Sci. & Tech., China
;
4
Amirkabir University of Technology, Iran, Islamic Republic of
Keyword(s):
Radon Transform, Content-based Image Retrieval, Binary Barcode, Radon Barcodes, Big Data.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Data Engineering
;
Feature Selection and Extraction
;
Image Understanding
;
Information Retrieval
;
Medical Imaging
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
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.
(More)