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Title: Local-driven semi-supervised learning with multi-label
Authors: Li, T.
Yan, S. 
Mei, T.
Kweon, I.-S.
Keywords: Image annotation
Local features
Multi-label learning
Semi-supervised learning
Issue Date: 2009
Citation: Li, T.,Yan, S.,Mei, T.,Kweon, I.-S. (2009). Local-driven semi-supervised learning with multi-label. Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009 : 1508-1511. ScholarBank@NUS Repository.
Abstract: In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally the decomposed labels on each vertex are further propagated to the unlabeled vertices based on the similarities measured according to the features extracted at each local regions. With the learnt local descriptor graph we can predict the semantic labels for not only the test local features but also the test images. The experiments on multi-label image annotation demonstrate the encouraging results from our proposed framework of semi-supervised learning. ©2009 IEEE.
Source Title: Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
ISBN: 9781424442911
DOI: 10.1109/ICME.2009.5202790
Appears in Collections:Staff Publications

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