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

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 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  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
 
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Download PDF pdficon (451 KB) | Citation | Downloads: 1,424 | Views: 5,038

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

Abstract
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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

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[CrossRef] [Web of Science Times Cited 125] [SCOPUS Times Cited 160]


[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.
[CrossRef] [SCOPUS Times Cited 23]


[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 3886] [SCOPUS Times Cited 5225]


[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 9510] [SCOPUS Times Cited 12269]


[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 33] [SCOPUS Times Cited 51]


[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 115] [SCOPUS Times Cited 143]


[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.

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[CrossRef] [SCOPUS Times Cited 124]


[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 1024] [SCOPUS Times Cited 1709]


[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.

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[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] [Web of Science Times Cited 4323] [SCOPUS Times Cited 6287]


[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 32]


[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.
[CrossRef]


[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 343] [SCOPUS Times Cited 435]


[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: 19,359 TCR
SCOPUS® Citations for all references: 26,458 TCR

Web of Science® Average Citations per reference: 807 ACR
SCOPUS® Average Citations per reference: 1,102 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 2024-05-23 23:10 in 85 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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