Semi-supervised Learning based on Bayesian networks and optimization for interactive image retrieval

In this paper, we present a novel interactive image retrieval technique using semi-supervised learning. Recently, Guan and Qiu [8, 9] have shown that by constructing a Bayesian Network where the nodes represent the (continuous) class membership scores and arcs represent the dependence relations of the data points, the (semi-supervised) classification problem can be formulated as a quadratic optimization problem; and by using the labeled data as linear constraints, the optimization problem yields a large, sparse system of linear equations which can be solved very efficiently using standard methods. In this work, we show that this semi-supervised learning method can be naturally adopted as a computational tool to incorporate users feedbacks for interactive image retrieval. We present experimental results to show the effectiveness of our new interactive image retrieval method. We also show that semi-supervised learning can have advantages over supervised and unsupervised learning in image retrieval applications.