Learning Graph Structure for Multi-Label Image Classification via Clique Generation

Mingkui Tan, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, Zhen Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4100-4109

Abstract


Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.

Related Material


[pdf]
[bibtex]
@InProceedings{Tan_2015_CVPR,
author = {Tan, Mingkui and Shi, Qinfeng and van den Hengel, Anton and Shen, Chunhua and Gao, Junbin and Hu, Fuyuan and Zhang, Zhen},
title = {Learning Graph Structure for Multi-Label Image Classification via Clique Generation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}