Retrieving similar X-ray images from big image data using radon barcodes with single projections

Morteza Babaie, H. R. Tizhoosh, Shujin Zhu, M. E. Shiri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages557-566
Number of pages10
ISBN (Electronic)9789897582226
DOIs
StatePublished - 2017
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: Feb 24 2017Feb 26 2017

Publication series

NameICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
Volume2017-January

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Country/TerritoryPortugal
CityPorto
Period2/24/172/26/17

Keywords

  • Big data
  • Binary barcode
  • Content-based image retrieval
  • Radon barcodes
  • Radon transform

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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