Discriminant-EM algorithm with application to image retrieval

In many vision applications, the practice of supervised learning faces several difficulties, one of which is that insufficient labeled training data result in poor generalization. In image retrieval, we have very few labeled images from query and relevance feedback so that it is hard to automatically weight image features and select similarity metrics for image classification. This paper investigates the possibility of including an unlabeled data set to make up the insufficiency of labeled data. Different from most current research in image retrieval, the proposed approach tries to cast image retrieval as a transductive learning problem, in which the generalization of an image classifier is only defined on a set of images such as the given image database. Formulating this transductive problem in a probabilistic framework the proposed algorithm, Discriminant EM (D-EM) not only estimates the parameters of a generative model but also finds a linear transformation to relax the assumption of probabilistic structure of data distributions as well as select good features automatically. Our experiments show that D-EM has a satisfactory performance in image retrieval applications. D-EM algorithm has the potential to many other applications.