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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

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


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  2/2020 - 10

Image Retrieval using One-Dimensional Color Histogram Created with Entropy

KILICASLAN, M. See more information about KILICASLAN, M. on SCOPUS See more information about KILICASLAN, M. on IEEExplore See more information about KILICASLAN, M. on Web of Science, TANYERI, U. See more information about  TANYERI, U. on SCOPUS See more information about  TANYERI, U. on SCOPUS See more information about TANYERI, U. on Web of Science, DEMIRCI, R. See more information about DEMIRCI, R. on SCOPUS See more information about DEMIRCI, R. on SCOPUS See more information about DEMIRCI, R. on Web of Science
 
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Download PDF pdficon (1,642 KB) | Citation | Downloads: 256 | Views: 414

Author keywords
entropy, feature extraction, histograms, image retrieval, vector quantization

References keywords
image(34), retrieval(22), content(10), quantization(7), entropy(7), histogram(6), vector(5), systems(5), method(5), information(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 79 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02010
Web of Science Accession Number: 000537943500010
SCOPUS ID: 85087460177

Abstract
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Image histograms are frequently used as a feature vector in content-based image retrieval (CBIR). The related methodology involves processing of a single channel histogram on gray level images while histograms of three channels must be processed in color images. Subsequently, there are two ways to process histograms of color images. In the first approach, the length of feature vector is extended by adding histogram data of each channel to create new feature vector. However, this kind of solution increases computational time and complexity. Second solution is to combine the histogram data obtained from each channel to establish a feature vector. In this study, a novel image retrieval approach, which uses a cluster-based one-dimensional histogram (ODH) for color images has been developed. Initially, multiple thresholds (MT) for each channel were calculated by means of Kapur entropy method. Then, the RGB color space was subdivided into sub-cubes or prisms. The numbers of pixels in each cluster and cluster index or class label have been used to construct a cluster-based one-dimensional histogram. Finally, image retrieval process has been implemented by using the one-dimensional color histogram (ODH) of images in database and query.


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References Weight

Web of Science® Citations for all references: 11,913 TCR
SCOPUS® Citations for all references: 15,892 TCR

Web of Science® Average Citations per reference: 265 ACR
SCOPUS® Average Citations per reference: 353 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

Citations for references updated on 2020-09-27 07:51 in 288 seconds.




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