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Pulse Coupled Neural Network based Near-Duplicate Detection of Images (PCNN - NDD)THYAGHARAJAN, K. K.   , KALAIARASI, G. |
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Author keywords
computer vision, copyright protection, feature extraction, image processing, neural networks
References keywords
image(46), neural(29), detection(22), duplicate(19), pulse(18), coupled(18), networks(12), network(12), retrieval(11), forgery(11)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 87 - 96
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03012
Web of Science Accession Number: 000442420900012
SCOPUS ID: 85052054688
Abstract
Near Duplicate images are variants of original image with some transformations / manipulations / forgeries in it. The illegal copies of images are identified to protect copyright enforcement and reduce redundancy. The existing works in ND detection are less accurate in the identification of similar images as near duplicates. Pulse Coupled Neural Network (PCNN) is found to be a suitable processor for all the image processing techniques including feature extraction. In this paper, PCNN is applied in the detection of near duplicate (ND) images. The proposed work Pulse Coupled Neural Network based Near Duplicate Detection of Images (PCNN-NDD) is a two-step process (1) feature extraction using PCNN and (2) fast image similarity measurement using correlation coefficient. Our system is capable of improving the accuracy effectively. The advantage of the proposed work lies in the proper setting of PCNN parameters to identify the similar images. The experimental results show that our PCNN-NDD system enhances the detection results and improves the accuracy when compared to other traditional systems. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
