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Image Classification by Cross-Media Active Learning with Privileged Information

Yan Yan, Feiping Nie, Wen Li, Chenqiang Gao, Yi Yang, and Dong Xu
IEEE Transactions on Multimedia (T-MM)
Vol. 18, No. 12, pp. 2494 - 2502, December 2016

Abstract

In this paper, we propose a novel cross-media active learning algorithm to reduce the effort on labeling images for training. The Internet images are often associated with rich textual descriptions. Even though such textual information is not available in test images, it is still useful for learning robust classifiers. In light of this, we apply the recently proposed supervised learning paradigm, learning using privileged information, to the active learning task. Specifically, we train classifiers on both visual features and privileged information, and measure the uncertainty of unlabeled data by exploiting the learned classifiers and slacking function. Then, we propose to select unlabeled samples by jointly measuring the cross-media uncertainty and the visual diversity. Our method automatically learns the optimal tradeoff parameter between the two measurements, which in turn makes our algorithms particularly suitable for real-world applications. Extensive experiments demonstrate the effectiveness of our approach.


Link to publisher's page
@Article{eth_biwi_01353,
  author = {Yan Yan and Feiping Nie and Wen Li and Chenqiang Gao and Yi Yang and and Dong Xu},
  title = {Image Classification by Cross-Media Active Learning with Privileged Information},
  journal = {IEEE Transactions on Multimedia (T-MM)},
  year = {2016},
  month = {December},
  pages = {2494 - 2502},
  volume = {18},
  number = {12},
  keywords = {}
}