Local-driven semi-supervised learning with multi-label

Teng Li, Shuicheng Yan, Tao Mei, In-So Kweon
2009 2009 IEEE International Conference on Multimedia and Expo  
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
more » ... n 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.
doi:10.1109/icme.2009.5202790 dblp:conf/icmcs/LiYMK09 fatcat:sdp3q37elra3tjk7dbkzeolguq