Click to open the HelpDesk interface
AECE - Front page banner



JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 81 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


2,066,795 unique visits
Since November 1, 2009

No robots online now


SCImago Journal & Country Rank

SEARCH ENGINES - Google Pagerank


Anycast DNS Hosting

 Volume 18 (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
 Volume 16 (2016)
     »   Issue 4 / 2016
     »   Issue 3 / 2016
     »   Issue 2 / 2016
     »   Issue 1 / 2016
 Volume 15 (2015)
     »   Issue 4 / 2015
     »   Issue 3 / 2015
     »   Issue 2 / 2015
     »   Issue 1 / 2015
  View all issues  


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.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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.

Read More »


  4/2011 - 18

New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure

GANEA, E. See more information about GANEA, E. on SCOPUS See more information about GANEA, E. on IEEExplore See more information about GANEA, E. on Web of Science, BURDESCU, D. D. See more information about  BURDESCU, D. D. on SCOPUS See more information about  BURDESCU, D. D. on SCOPUS See more information about BURDESCU, D. D. on Web of Science, BREZOVAN, M. See more information about BREZOVAN, M. on SCOPUS See more information about BREZOVAN, M. on SCOPUS See more information about BREZOVAN, M. 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 (451 KB) | Citation | Downloads: 961 | Views: 2,809

Author keywords
feature extraction, image processing, image segmentation, hypergraph data structures, object detection

References keywords
segmentation(12), image(12), pattern(10), vision(8), recognition(6), graph(6), multimedia(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 111 - 116
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04018
Web of Science Accession Number: 000297764500018
SCOPUS ID: 84856623803

Quick view
Full text preview
This paper presents a method for detection of salient objects from images. The proposed algorithms for image segmentation and objects detection use a hexagonal representation of the image pixels and a hypergraph structure to process this hierarchal structure. The main goal of the method is to obtain salient regions, which may be associated with semantic labels. The designed algorithms use color characteristic and syntactic features for image segmentation. The object-oriented model used for storing the results of the segmentation and detection allows directly annotation of regions without a processing of these. The experiments showed that the presented method is robust and accurate comparing with others public methods used for salient objects detection.

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

[1] D. Jacobs. "Robust and efficient detection of salient convex groups", In IEEE Transaction on Pattern Analysis and Machine Intelligence, pp. 23 - 37, 1996.
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 150]

[2] S. Rital, H. Cherifi and S. Miguet. "Weighted Adaptive Neighborhood Hypergraph Partitioning for Image Segmentation", Lecture Notes in Computer Science, 3687, pp. 522 - 531, 2005.

[3] C. F. Bennstrom and J. R. Casas. "Binary-partition-tree creation using a quasi-inclusion criterion", In Proceedings of the Eighth International Conference on Information Visualization, London, UK, pp. 259 - 294, 2004.

[4] P. F. Felzenszwalb and W. D. Huttenlocher. "Efficient Graph-Based Image Segmentation", International Journal of Computer Vision, pp. 167 - 181, 2004.
[CrossRef] [Web of Science Times Cited 2355] [SCOPUS Times Cited 3494]

[5] J. Shi and J. Malik. "Normalized cuts and image segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 731 - 737, 2000.
[CrossRef] [Web of Science Times Cited 5803] [SCOPUS Times Cited 8402]

[6] R. Urquhar. "Graph theoretical clustering based on limited neighborhood sets", In Pattern Recognition Letters, 15, pp. 173 - 187, 1982.

[7] L. Guigues, L. M. Herve and L.-P. Cocquerez. "The hierarchy of the cocoons of a graph and its application to image segmentation", In Pattern Recognition Letters, 24, pp. 1059 - 1066, 2003.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 47]

[8] Y. Gdalyahu, D. Weinshall and M. Werman. "Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization", In IEEE Transaction on Pattern Analysis and Machine Intelligence, 23, pp. 1053 - 1074, 2001.
[CrossRef] [Web of Science Times Cited 102] [SCOPUS Times Cited 115]

[9] T. Adamek, N. E. O'Connor and N. Murphy. "Region-based segmentation of images using syntactic visual features", In WIAMIS 2005 - 6th International Workshop on Image Analysis for Multimedia Interactive Services, 2005.

[10] T. Athanasiadis, V. Tzouvaras Petridis, K. F. Precioso, Y. Avrithis and I. Kompatsiaris. "Using a Multimedia Ontology Infrastructure for Semantic Annotation of Multimedia Content", In The 5th International Workshop on Knowledge Markup and Semantic Annotation at the 4th International Semantic Web Conference, Galway, Ireland, 2005.

[11] J. R. Smith and S.-F. Chang. "VisualSEEk: a Fully Automated Content-Based Image Query System", In ACM Multimedia, Boston, MA, pp. 87 - 98, 1996.
[CrossRef] [SCOPUS Times Cited 53]

[12] A. Bretto and L. Gillibert. "Hypergraph-based imge representation", In Graph-Based Representations in Pattern Recognition, pp. 1-11, 2005.

[13] E. Ganea and M. Brezovan. "An Hypegraph Object-Oriented Model for Image Segmentation and Annotation", In Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 695 - 701, 2010.

[14] C. Forgy. "Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem", In Artificial Intelligence, 19, pp. 17 - 37, 1982.
[CrossRef] [Web of Science Times Cited 897] [SCOPUS Times Cited 1423]

[15] J. B. Kruskal, "On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem", Proceedings of the American Mathematical Society, vol. 7, no. 1, pp. 48-50, 1956.

[16] O. Boruvka. "O jistem problemu minimalnim (About a certain minimal problem)", In Prace mor. Prirodoved, pp. 37-58, 1926.

[17] I. Jonyer, L. Holder and D. Cook. "Concept Formation Using Graph Grammars", In Proceedings of the KDD Workshop on Multi-Relational Data Mining, 2002.

[18] L. B. Holder. "Empirical Substructure Discovery", In Proceedings of the Sixth International Workshop on Machine Learning, pp. 133-136, 1989.

[19] D. Martin, C. Fowlkes, D. Tal and J. Malik. "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", In IEEE International Conference on Computer Vision, pp. 416 - 423, 2001.
[CrossRef] [SCOPUS Times Cited 2725]

[20] M. Donoser and H. Bischof. "ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results", In IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
[CrossRef] [SCOPUS Times Cited 31]

[21] C. Fowlkes, D. Martin and J. Malik. "Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 54 - 61, 2003.

[22] V. Movahedi and J. H. Elder. "Segmenting Salient Objects: How do we measure success?", Poster at CVR09, Centre for Vision Research CVR Conference, 2009.

[23] A. Y Yang., J. Wright, M. Yi and S. S. Sastry. "Unsupervised Segmentation of Natural Images via Lossy Data Compression", In Computer Vision and Image Understanding, vol. 110, pp. 212 - 225, 2008.
[CrossRef] [Web of Science Times Cited 230] [SCOPUS Times Cited 303]

[24] F. Ge, S. Wang and T. Liu. "New benchmark for image segmentation evaluation", In Journal of Electronic Imaging, vol. 16, 2007.

References Weight

Web of Science® Citations for all references: 9,535 TCR
SCOPUS® Citations for all references: 16,743 TCR

Web of Science® Average Citations per reference: 397 ACR
SCOPUS® Average Citations per reference: 698 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 2018-10-12 04:25 in 89 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 ©2001-2018
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: