Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 1.221
JCR 5-Year IF: 0.961
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: Aug 2021
Next issue: Nov 2021
Avg review time: 92 days


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


1,749,399 unique visits
Since November 1, 2009

No robots online now


SCImago Journal & Country Rank


Anycast DNS Hosting

 Volume 21 (2021)
     »   Issue 3 / 2021
     »   Issue 2 / 2021
     »   Issue 1 / 2021
 Volume 20 (2020)
     »   Issue 4 / 2020
     »   Issue 3 / 2020
     »   Issue 2 / 2020
     »   Issue 1 / 2020
 Volume 19 (2019)
     »   Issue 4 / 2019
     »   Issue 3 / 2019
     »   Issue 2 / 2019
     »   Issue 1 / 2019
 Volume 18 (2018)
     »   Issue 4 / 2018
     »   Issue 3 / 2018
     »   Issue 2 / 2018
     »   Issue 1 / 2018
 Volume 17 (2017)
     »   Issue 4 / 2017
     »   Issue 3 / 2017
     »   Issue 2 / 2017
     »   Issue 1 / 2017
  View all issues  


Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Read More »


  4/2010 - 8


From Content-Based Image Retrieval by Shape to Image Annotation

MOCANU, I. See more information about MOCANU, I. on SCOPUS See more information about MOCANU, I. on IEEExplore See more information about MOCANU, I. on Web of Science
View the paper record and citations in View the paper record and citations in Google Scholar
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 (1,949 KB) | Citation | Downloads: 1,156 | Views: 3,920

Author keywords
image annotation, image representations, image retrieval by content, genetic algorithm, shape retrieval

References keywords
image(11), retrieval(10), information(6), systems(5), shape(5), multimedia(5), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-11-30
Volume 10, Issue 4, Year 2010, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.04008
Web of Science Accession Number: 000284782700008
SCOPUS ID: 78649708171

Quick view
Full text preview
In many areas such as commerce, medical investigations, and others, large collections of digital images are being created. Search operations inside these collections of images are usually based on low-level features of objects contained in an image: color, shape, texture. Although such techniques of content-based image retrieval are useful, they are strongly limited by their inability to consider the meaning of images. Moreover, specifying a query in terms of low level features may not be very simple. Image annotation, in which images are associated with keywords describing their semantics, is a more effective way of image retrieval and queries can be naturally specified by the user. The paper presents a combined set of methods for image retrieval, in which both low level features and semantic properties are taken into account when retrieving images. First, it describes some methods for image representation and retrieval based on shape, and proposes a new such method, which overcomes some of the existing limitations. Then, it describes a new method for image semantic annotation based on a genetic algorithm, which is further improved from two points of view: the obtained solution value - using an anticipatory genetic algorithm, and the execution time - using a parallel genetic algorithm.

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

[1] T. Kato, "Database architecture for content-based image retrieval", Proc. SPIE 1662, vol. 112, pp. 112-123, Feb. 1992.

[2] L. R. B. Schomaker, E. de Leau and L. G. Vuurpijl, "Using pen-based outlines for object-based annotation and image-based queries", Visual Information and Information Systems, pp. 585-592, 1999.

[3] G. Lu. Multimedia Database Management Systems. Artech House Publishers, Boston, 1999.

[4] E. Persoon and K. S. Fu, "Shape discrimination using Fourier Descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, pp. 388-397, 1986.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 185]

[5] C. Zahn and R. Roskies, "Fourier descriptors for plane closed curves", IEEE Trans. On Computer, vol. 21, pp. 269-281, 1972.
[CrossRef] [SCOPUS Times Cited 1444]

[6] D. Zhang and G. Lu, "A comparative study on shape retrieval using fourier descriptors with different shape signatures", Proceedings of International Conference on Intelligent Multimedia and Distance Education, pp. 1-9, 2001.

[7] E. M. Arkin, L. P. Chew, D. P. Huttenlocher, K. Kedem and J. S. B. Mitchell, "An efficiently computable metric for comparing polygonal shapes", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 3, pp. 209-216, 1991.
[CrossRef] [Web of Science Times Cited 339] [SCOPUS Times Cited 477]

[8] K. L. Tan,, B. C. Ooi and L. F. Thiang, "Retrieving similar shapes efficiently", Multimedia Tools and Applications, vol. 19, no. 2, pp. 111-134, 2003.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]

[9] S. Fan, "Shape representation and retrieval using distance histograms", Technical Report of University of Alberta, 2001.

[10] I. Mocanu, "Image retrieval by shape based on contour techniques: a comparative study", Applied Computational Intelligence and Informatics, pp. 219-223, 2007.

[11] L. P. Cordella and G. Dettori, "An O(N) algorithm for polygonal approximation", Pattern Recognition Letters, vol. 3, pp. 93-97, 1985.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]

[12] F. Mokhtarian, J. Kittler and S. Abbasi, "The Squid project", in press.

[13] I. Mocanu, "Optimization algorithms in multimedia databases", PhD Thesis.

[14] J. P. Eakins, "Automatic image content retrieval - are we getting anywhere ?", Proceedings of Third International Conference on Electronic Library and Visual Information Research, pp. 123-135, 1996.

[15] J. P. Eakins, "Techniques for image retrieval", Library and Information Briefings, in press.

[16] V. N. Gudivada and V. V. Raghavan, "Content-based image retrieval systems", IEEE Computer, vol. 28, no. 9, pp. 18-22, 1995.

[17] Y. Mori, H. Takahashi, and R. Oka, "Image-to-word transformation based on dividing and vector quantizing images with words", Proceedings of the International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.

[18] P. Duygulu, K. Barnard, J. F. G. de Freitas and D. A. Forsyth, "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary", European Conference on Computer Vision, pp. 97-112, 2002.

[19] J. Jeon, V. Lavrenko and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models", ACM SIGIR, pp. 119-126, 2003.

[20] P. Panagi, S. Dasiopoulou, G. Th. Papadopoulos, I. Kompatsiaris and M. G. Strintzis, "A genetic algorithm approach to ontology-driven demantic image analysis", IET International Conference on Visual Information Engineering, pp. 132-137, 2006.

[21] S. Jeong, "Histogram-based color image retrieval", Psych221/EE362 Project Report. Mar.15, 2001.

[22] M. J. Swain and D. H. Ballard, D. H., "Color indexing", International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[CrossRef] [Web of Science Times Cited 3322] [SCOPUS Times Cited 4337]

[23] J. B. MacQueen, "Some methods for classification and analysis of multivariate observations", Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp 281-297, 1967.

[24] I. Mocanu, E. Kalisz and L. Negreanu, "Genetic algorithms viewed as anticipatory systems", AIP Conference Proceedings, to be published.

[25] M. Nowostawski and R. Poli, "Parallel genetic algorithm taxonomy", Knowledge-Based Intelligent Information Engineering Systems, pp. 88-92, 1999.

References Weight

Web of Science® Citations for all references: 3,825 TCR
SCOPUS® Citations for all references: 6,482 TCR

Web of Science® Average Citations per reference: 147 ACR
SCOPUS® Average Citations per reference: 249 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 2021-10-18 04:26 in 50 seconds.

Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2021
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.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

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.

Website loading speed and performance optimization powered by: