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  3/2018 - 12

 HIGH-IMPACT PAPER 

Pulse Coupled Neural Network based Near-Duplicate Detection of Images (PCNN - NDD)

THYAGHARAJAN, K. K. See more information about THYAGHARAJAN, K. K. on SCOPUS See more information about THYAGHARAJAN, K. K. on IEEExplore See more information about THYAGHARAJAN, K. K. on Web of Science, KALAIARASI, G. See more information about KALAIARASI, G. on SCOPUS See more information about KALAIARASI, G. on SCOPUS See more information about KALAIARASI, G. on Web of Science
 
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Download PDF pdficon (1,458 KB) | Citation | Downloads: 914 | Views: 2,060

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
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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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 8,346 TCR
SCOPUS® Citations for all references: 12,140 TCR

Web of Science® Average Citations per reference: 119 ACR
SCOPUS® Average Citations per reference: 173 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

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