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 databases. 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 distribution 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.
|