@inproceedings{c7e9bc27d91b4069ae69459361e078b6,
title = "Local radon descriptors for image search",
abstract = "Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as content-based medical image retrieval. However, all studies so far have used Radon transform as a global or quasi-global image descriptor by extracting projections of the whole image or large sub-images. This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply it to the IRMA dataset, which contains 14,410 x-ray images as well as to the INRIA Holidays dataset with 1,990 images. Our results show significant improvement in retrieval performance by using LRD versus its global version. We also demonstrate that LRD can deliver results comparable to well-established descriptors like LBP and HOG.",
keywords = "IRMA dataset, Image retrieval, Radon projections, image descriptor, local projections",
author = "Morteza Babaie and Tizhoosh, {H. R.} and Amin Khatami and Shiri, {M. E.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017 ; Conference date: 28-11-2017 Through 01-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/IPTA.2017.8310144",
language = "English (US)",
series = "Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017",
}