|4/2011 - 18|
New Method to Detect Salient Objects in Image Segmentation using Hypergraph StructureGANEA, E. , BURDESCU, D. D. , BREZOVAN, M.
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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)
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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 116] [SCOPUS Times Cited 149]
 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 2292] [SCOPUS Times Cited 3394]
 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 5676] [SCOPUS Times Cited 8172]
 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 32] [SCOPUS Times Cited 47]
 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 101] [SCOPUS Times Cited 114]
 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 48]
 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 896] [SCOPUS Times Cited 1411]
 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 2641]
 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 30]
 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 226] [SCOPUS Times Cited 295]
 F. Ge, S. Wang and T. Liu. "New benchmark for image segmentation evaluation", In Journal of Electronic Imaging, vol. 16, 2007.
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