Research on Uncertainty of Video Content Retrieval Based on Semantic

Article Preview

Abstract:

Nowadays, video media data is already facilitates generation, transmission, storage and circulation on the global scale. Video data is geometrically fast as the rate of growth, the video data processing and analysis have lagged behind the pace of development in the growth of data, resulting in large amounts of data is wasted. Therefore, it becomes an urgent need for efficient retrieval of video data content. In this paper, firstly, starting from the color feature, the color space of digital mapping and semantic color space conversion technology is proposed according to the problem of Semantic concepts for video does not match with the perceived characteristics. And then we realize the mapping from the low-level features to high-level semantic. Finally, semantic rules of uncertainty reasoning based on cloud model established to complete video content retrieval.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

814-817

Citation:

Online since:

February 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Peter Wilkins, Alan F. Smeaton, Noel E. O'Connor et al., K-Space Interactive Search, Proceedings of the 2008 ACM International Conference on Content-Based Image and Video Retrieval, Niagara Falls, Canada, 2008, pp.555-556.

DOI: 10.1145/1386352.1386428

Google Scholar

[2] Ork de Rooij, Cees G.M. Snoek, Marcel Worring, Mediamill: Fast and Effective Video Search Using the Forkbrowser, Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada, 2008, pp.561-562.

DOI: 10.1145/1386352.1386431

Google Scholar

[3] Shi-Yong Neo, Huanbo Luan, Yantao Zheng et al., Visiongo: Bridging Users and Multimedia Video Retrieval, Proceedings of the 2008 international conference on Content-based image and video retrieval, Niagara Falls, Canada, 2008, pp.559-560.

DOI: 10.1145/1386352.1386430

Google Scholar

[4] Huang J.,S. R. Kumar, Image Indexing Using Color Correlogram, IEEE Conference of ter Vision Pattern Recognition (CVPR 97), San. Juan, Puerto Rico, 1997, pp.762-768.

Google Scholar

[5] Stricken M.,M. Orengo, Similarity of Color, SPIE Storage and Retrieval for Image and Viedeo Databases III, vol. 2185, pp.381-392, (1995).

Google Scholar

[6] Ma, W. -Y,B. S. Manjunath, A Comparison of Wavelet Transform Features for Texture Image Annotation, Proceedings of IEEE International Conference on Image Processing (ICIP), pp.256-259, (1995).

DOI: 10.1109/icip.1995.537463

Google Scholar

[7] Liu W. Y, Z. Su, et al., A Performance Evaluation Protocol for Content-Based Image Retrieval Algorithms/Systems., IEEE CVPR Workshop on Empirical Evaluation in Computer Vision, Kauai, Hawaii, USA, (2001).

Google Scholar

[8] M. Naphade, J. R. Smith, J. Tesic et al., Large-Scale Concept Ontology for Multimedia, Multimedia, IEEE, vol. 13, no. 3, pp.86-91, (2006).

DOI: 10.1109/mmul.2006.63

Google Scholar