Automatic image annotation based on co-training

Abstract

Automatic image annotation is a critical and challenging problem in pattern recognition and image understanding areas. There are some problems in existing automatic image annotation areas. For example, the size of unlabeled data is much larger than the labeled data. Besides, most image annotation models can only use one kind of image segmentation strategy and certain image description method. According to the above problems, an automatic image annotation model based on Co-training is proposed. In this model, four independent feature properties are constructed and then four corresponding sub-classifers are built. In this way, different image segmentation strategies and feature representation methods can be integrated into a unified framework. An adaptive algorithm based on vote and consistency is proposed to extend the training dataset. The proposed method use Co-training algorithm and mass unlabeled data to improve the performance of automatic image annotation. Experiments conducted on Corel 5 K dataset verify the effectiveness of proposed method.