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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  3/2018 - 12

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
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,458 KB) | Citation | Downloads: 219 | Views: 362

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

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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: 3,516 TCR
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Web of Science® Average Citations per reference: 50 ACR
SCOPUS® Average Citations per reference: 0

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