Two-Dimensional Active Learning for image classification

In this paper, we propose a two-dimensional active learning scheme and show its application in image classification. Traditional active learning methods select samples only along the sample dimension. While this is the right strategy in binary classification, it is sub-optimal for multilabel classification. In multi-label classification, we argue that, for each selected sample, only a part of more effective labels are necessary to be annotated while others can be inferred by exploring the correlations among the labels. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multi-label Bayesian classification error bound. This new active learning strategy not only considers the sample dimension but also the label dimension, and we call it Two-Dimensional Active Learning (2DAL). We also show that the ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang, Hong-Jiang Zhang
• Keywords: Active Learning Formulation, Active Learning Scheme, Active Learning Strategy, Computer Vision, CVPR 2008, Traditional Active Learning, Two-dimensional Active Learning