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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 268-270Research on High-Level Semantic Image Retrieval

Research on High-Level Semantic Image Retrieval

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Abstract:

This paper presented the key problems to shorten “semantic gap” between low-level visual features and high-level semantic features to implement high-level semantic image retrieval. First, introduced ontology based semantic image description and semantic extraction methods based on machine learning. Then, illustrated image grammar on the high-level semantic image understanding and retrieval, and-or graph and context based methods of semantic image. Finally, we discussed the development directions and research emphases in this field.

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Computational Materials Science

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Periodical:

Advanced Materials Research (Volumes 268-270)

Pages:

1427-1432

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.268-270.1427

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Online since:

July 2011

Authors:

Chang Yong Ri, Min Yao

Keywords:

And-Or Graph, Context Image Feature, Image Grammar, Machine Learning (ML), Ontology, Semantic Image Retrieval

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] F. Jing, M. Li, L. Zhang, H. J. Zhang, B. Zhang, Learning in region-based image retrieval, Proc. Int. Conf. on Image and Video Retrieval (CIVR2003), p.206–215.

DOI: 10.1007/3-540-45113-7_21

[2] H. Tamura, S. Mori, T. Yamawaki, Texture features corresponding to visual perception, IEEE Trans. Syst. Man Cybern., 8 (6) (1978), p.460–473.

DOI: 10.1109/tsmc.1978.4309999

[3] R. Mehrotra, J. E. Gary, Similar-shape retrieval in shape data management, IEEE Comput., 28 (9) (1995), p.57–62.

DOI: 10.1109/2.410154

[4] V. Mezaris, I. Kompatsiaris, M.G. Strintzis, An ontology approach to object-based image retrieval, Proc. ICIP, 2 (2003), p.511–514.

DOI: 10.1109/icip.2003.1246729

[5] T. Berk, L. Brownston, A. Kaufman, A new color-naming system for graphics language, IEEE Comput., Graphics Appl. 2 (3) (1982), p.37–44.

DOI: 10.1109/mcg.1982.1674223

[6] A. R. Rao, G. L. Lohse, Towards a texture naming system: identifying relevant dimensions of texture, IEEE Proceedings of the Fourth Conference on Visualization, 1993, p.220–227.

DOI: 10.1109/visual.1993.398872

[7] P. L. Stanchev, D. J. Green, B. Dimitrov, High level color similarity retrieval, Int. J. Inf., Theories Appl. 10 (3) (2003) p.363–369.

[8] Y. Liu, D. S. Zhang, G. Lu, W. Y. Ma, Region-based image retrieval with perceptual colors, Proc. Pacific-Rim Multimedia Conference (PCM), 2004, p.931–938.

DOI: 10.1007/978-3-540-30542-2_115

[9] R. Shi, H. Feng, et al., An adaptive image content representation and segmentation approach to automatic image annotation, Int. Conf. on Image and Video Retrieval (CIVR), 2004, p.545–554.

DOI: 10.1007/978-3-540-27814-6_64

[10] W. Jin, R. Shi, T. S. Chua, A semi-naïve Bayesian method incorporating clustering with pair-wise constraints for auto image annotation, Proc. ACM Multimedia, (2004).

DOI: 10.1145/1027527.1027605

[11] C. Cusano, G. Ciocca, R. Schettini, Image annotation using SVM, Proc. SPIE, 2004, p.330–338.

[12] Y. L. Gao, et al., Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers, Proc. 14th ACM Int. Conf. on Multimedia, 2006, p.901–910.

DOI: 10.1145/1180639.1180840

[13] J. B. Luo, et al., Indoor vs outdoor classification of consumer photographs using low-level and semantic features, Proc. IEEE Int. Conf. on Image Processing, 2001, p.745–748.

DOI: 10.1109/icip.2001.958601

[14] A. Vailaya, M. A. T. Figueiredo, A. K. Jain, et al., Image classification for content-based indexing, IEEE Transactions on Image Processing, 2001, l0 (1), p.117–130.

DOI: 10.1109/83.892448

[15] G. Carneiro, et al., Supervised learning of semantic classes for image annotation and retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29 (3), p.394–4l0.

DOI: 10.1109/tpami.2007.61

[16] D. Stan, I. K. Sethi, Mapping low-level image features to semantic concepts, Proc. SPIE: Storage and Retrieval for Media Databases, 2001, p.172–179.

DOI: 10.1117/12.410925

[17] M. Bilenko, et al., Integrating constraints and metric learning in semi-supervised clustering, Proc. 21st Int. Conf. on Machine Learning (ICML), July 2004, p.81–88.

DOI: 10.1145/1015330.1015360

[18] S. H. Zhu, D. Mumford, A Stochastic Grammar of Images, http: /www. lotushill. org.

[19] J. Porway, et al., A Hierarchical and Contextual Model for Aerial Image Understanding, Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, (2008).

DOI: 10.1109/cvpr.2008.4587359

[20] I. Biederman, R. J. Mezzanotte, J. C. Rabinowitz, Scene Perception: Detecting and Judging Objects Undergoing Relational Violations, Cognitive Psychology, 1982, 14 (2), p.143–177.

DOI: 10.1016/0010-0285(82)90007-x

[21] C. Galleguillos, S. Belongie, Context Based Object Categorization: A Critical Survey, Technical Report, USA: University of California. Department of Computer Science and Engineering, (2008).

[22] Y. G. Zhang, M. Yao, Y. Zhen, Research on methodology of image semantic understanding based on generalized computing, Proc. MIPPR, (2007).