Multi-label active learning for image classification with asymmetrical conditional dependence
Jian Wu, Shiquan Zhao, Victor S. Sheng, Pengpeng Zhao, Zhiming Cui · 2016
Image classification is a hot topic of pattern recognition in computer vision. In order to achieve high accuracy of classification, a certain amount of high quality pictures are needed. As a matter of fact, high quality pictures are scarce. Active learning can solve such a problem. Label dependences play an important role in multi-label active learning for image classification. The interdependences between different labels are usually different and asymmetrical. This paper first brings the asymmetrical conditional label dependences into a novel active learning method for multi-label image classification based on the asymmetrical conditional label dependence, called ACDAL. Our extensive experimental results on three image and two non-image datasets show that our new approach ACDAL significantly outperforms existing approaches.