Uncertainty in Artificial Intelligence
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Multi-label Image Classification with A Probabilistic Label Enhancement Model
Xin Li, Feipeng Zhao, Yuhong Guo
In this paper, we present a novel probabilistic la- bel enhancement model to tackle multi-label im- age classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the ob- jects and occlusions. We propose to tackle these difficulties from a novel perspective by construct- ing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the la- bel space and improve the label identification ca- pacity in the original label space. In particular, we identify a set of informative label combina- tion pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a condi- tional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise train- ing under the framework of conditional random fields. In the test phase, max-product message passing is used to perform efficient inference on the tree graph, which integrates the augmented label pair classifiers and the standard individual binary classifiers for multi-label prediction. We evaluate the proposed approach on several image classification datasets. The experimental results demonstrate the superiority of our label enhance- ment model in terms of both prediction perfor- mance and running time comparing to the-state- of-the-art multi-label learning methods.
Pages: 430-439
PS Link:
PDF Link: /papers/14/p430-li.pdf
AUTHOR = "Xin Li and Feipeng Zhao and Yuhong Guo",
TITLE = "Multi-label Image Classification with A Probabilistic Label Enhancement Model",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "430--439"

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