Graph-based semi-supervised learning with multi-label
Zheng-Jun Zha
Tao Mei
Jingdong Wang
Zengfu Wang
Xian-Sheng Hua
Univ. of Sci. & Technol. of China, Hefei;
This paper appears in: Multimedia and Expo, 2008 IEEE International Conference on
Publication Date: June 23 2008-April 26 2008
On page(s): 1321-1324
Location: Hannover,
ISBN: 978-1-4244-2570-9
INSPEC Accession Number: 10178982
Digital Object Identifier: 10.1109/ICME.2008.4607686
Current Version Published: 2008-08-26
Abstract
Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multi-label. The proposed approach is characterized by simultaneously exploiting the inherent correlations among multiple labels and the label consistency over the graph. We apply the proposed framework to video annotation and report superior performance compared to key existing approaches over the TRECVID 2006 corpus.
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