Multi-label Image Classification with A Probabilistic Label Enhancement Model

Xin Li Temple University, Feipeng Zhao Temple University, Yuhong Guo Temple University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:902-911, 2014.

Abstract

In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the label space and improve the label identification capacity in the original label space. In particular, we identify a set of informative label combination pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a conditional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise training 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 enhancement model in terms of both prediction performance and running time comparing to the-state-of-the-art multi-label learning methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14y, title = {Multi-label Image Classification with A Probabilistic Label Enhancement Model}, author = {University, Xin Li Temple and University, Feipeng Zhao Temple and University, Yuhong Guo Temple}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {902--911}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14y/university14y.pdf}, url = {https://proceedings.mlr.press/r12/university14y.html}, abstract = {In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the label space and improve the label identification capacity in the original label space. In particular, we identify a set of informative label combination pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a conditional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise training 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 enhancement model in terms of both prediction performance and running time comparing to the-state-of-the-art multi-label learning methods.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Multi-label Image Classification with A Probabilistic Label Enhancement Model %A Xin Li Temple University %A Feipeng Zhao Temple University %A Yuhong Guo Temple University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14y %I PMLR %P 902--911 %U https://proceedings.mlr.press/r12/university14y.html %V R12 %X In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the label space and improve the label identification capacity in the original label space. In particular, we identify a set of informative label combination pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a conditional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise training 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 enhancement model in terms of both prediction performance and running time comparing to the-state-of-the-art multi-label learning methods. %Z Reissued by PMLR on 04 October 2026.
APA
University, X.L.T., University, F.Z.T. & University, Y.G.T.. (2014). Multi-label Image Classification with A Probabilistic Label Enhancement Model. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:902-911 Available from https://proceedings.mlr.press/r12/university14y.html. Reissued by PMLR on 04 October 2026.

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