Semi-supervised Learning for Automatic Image Annotation Based on Bayesian Framework
Dongping Tian · International Journal of Control and Automation · 2014
In this paper, we present a new method for automatic image annotation by applying semisupervised learning based on the Bayesian framework.On the one hand, we employ the semisupervised learning, i.e., transductive support vector machine (TSVM) to enhance the quality of training image data, which is a promising way to find out the underlying relevant data from the unlabeled ones.On the other hand, a simple yet very efficient Bayesian model is built to implement image annotation by the maximum a posteriori (MAP) criterion.The novelty of our method mainly lies in two aspects: exploiting TSVM to improve the quality of training image dataset and utilizing the Bayesian model to predict the candidate annotations for the unseen images.Experimental results on the standard Corel dataset demonstrate that the proposed method is superior or highly competitive to several state-of-the-art approaches.