Local-driven semi-supervised learning with multi-label

Authors:
Teng Li;Shuicheng Yan;Tao Mei;In-So Kweon
Affiliations:
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology;Department of Electrical and Computer Engineering, National University of Singapore;Microsoft Research Asia, Beijing, P. R. China;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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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.