Multi-label image classification with a probabilistic label enhancement model
Xin Yan Li, Feipeng Zhao, Yuhong Guo · 2014
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. 1