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Google challenge: incremental-learning for web video categorization on robust semantic feature space

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International Multimedia Conference archive
Proceedings of the seventeen ACM international conference on Multimedia table of contents

Beijing, China

SESSION: Multimedia grand challenge table of contents

Pages 1113-1114

Year of Publication: 2009

ISBN:978-1-60558-608-3

Authors

Yicheng Song	Institute of Computing Technology, Chinese Academy of Science, Beijing, China
Yong-dong Zhang	Institute of Computing Technology, Chinese Academy of Science, Beijing, China
Xu Zhang	Institute of Computing Technology, Chinese Academy of Science, Beijing, China
Juan Cao	Institute of Computing Technology, Chinese Academy of Science, Beijing, China
Jing-Tao Li	Institute of Computing Technology, Chinese Academy of Science, Beijing, China

Sponsor

SIGMULTIMEDIA: ACM Special Interest Group on Multimedia

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ACM New York, NY, USA

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ABSTRACT

With the advent of video sharing websites, the amount of videos on the internet grows rapidly. Web video categorization is an efficient methodology to organize the huge amount of data. In this paper, we propose an effective web video categorization algorithm for the large scale dataset. It includes two factors: 1) For the great diversity of web videos, we develop an effective semantic feature space called Concept Collection for Web Video Categorization (CCWV-CD) to represent web videos, which consists of concepts with small semantic gap and high distinguishing ability. Meanwhile, the online Wikipedia API is employed to diffuse the concept correlations in this space. 2) We propose an incremental support vector machine with fixed number of support vectors (n-ISVM) to fit the large scale incremental learning problem in web video categorization. Extensive experiments are conducted on the dataset of 80024 most representative videos on YouTube demonstrate that the semantic space with Wikipedia prorogation is more representative for web videos, and n-ISVM outperforms other algorithms in efficiency when performs the incremental learning.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	J. Cao, Y.D. Zhang, Y.C. Song, Z.N. Chen, X. Zhang, J.T. Li. MCG-WEBV: A Benchmark Dataset for Web Video Analysis. Technical Report, MCG-ICT-CAS-09-001, May 2009.
	2	K. a. H. Nakayama, T. and Nishio, S., Wikipedia Mining -- Wikipedia as a Corpus for Knowledge Extraction, in Proceedings of Annual Wikipedia Conference (Wikimania), 2008.
	3	L. Pavel, G. Christian, K. Stefan, ger, M. Klaus-Robert, and ller, Incremental Support Vector Learning: Analysis, Implementation and Applications, J. Mach. Learn. Res., vol. 7, pp. 1909--1936, 20.
	4	T.S Chua, J.H. Tang, R. C. Hong, H.J. Li, Z.P. Luo, and Y.T Zheng. NUS-WIDE: A Real-World Web Image Database from National University of Singapore, ACM International Conference on Image and Video Retrieval. Greece, 2009.