|4/2011 - 18|
New Method to Detect Salient Objects in Image Segmentation using Hypergraph StructureGANEA, E. , BURDESCU, D. D. , BREZOVAN, M.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (451 KB) | Citation | Downloads: 947 | Views: 2,572|
feature extraction, image processing, image segmentation, hypergraph data structures, object detection
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
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|
| 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 115] [SCOPUS Times Cited 146]
 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.
 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.
 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 2037] [SCOPUS Times Cited 3130]
 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 5206] [SCOPUS Times Cited 7685]
 R. Urquhar. "Graph theoretical clustering based on limited neighborhood sets", In Pattern Recognition Letters, 15, pp. 173 - 187, 1982.
 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 31] [SCOPUS Times Cited 46]
 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 99] [SCOPUS Times Cited 112]
 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.
 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.
 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 30]
 A. Bretto and L. Gillibert. "Hypergraph-based imge representation", In Graph-Based Representations in Pattern Recognition, pp. 1-11, 2005.
 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.
 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 871] [SCOPUS Times Cited 1367]
 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.
 O. Boruvka. "O jistem problemu minimalnim (About a certain minimal problem)", In Prace mor. Prirodoved, pp. 37-58, 1926.
 I. Jonyer, L. Holder and D. Cook. "Concept Formation Using Graph Grammars", In Proceedings of the KDD Workshop on Multi-Relational Data Mining, 2002.
 L. B. Holder. "Empirical Substructure Discovery", In Proceedings of the Sixth International Workshop on Machine Learning, pp. 133-136, 1989.
 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 2282]
 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 25]
 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.
 V. Movahedi and J. H. Elder. "Segmenting Salient Objects: How do we measure success?", Poster at CVR09, Centre for Vision Research CVR Conference, 2009.
 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 195] [SCOPUS Times Cited 262]
 F. Ge, S. Wang and T. Liu. "New benchmark for image segmentation evaluation", In Journal of Electronic Imaging, vol. 16, 2007.
Web of Science® Citations for all references: 8,554 TCR
SCOPUS® Citations for all references: 15,085 TCR
Web of Science® Average Citations per reference: 356 ACR
SCOPUS® Average Citations per reference: 629 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 2017-12-08 15:00 in 89 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
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.
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.