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 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.

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 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.}, 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 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. %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.

Related Material