Towards Self-Exploring Discriminating Features

Many visual learning tasks are usually confronted by some common diiculties. One of them is the lack of supervised information, due to the fact that labeling could be tedious, expensive or even impossible. Such scenario makes it challenging to learn object concepts from images. This problem could be alleviated by taking a hybrid of labeled and unlabeled training data for learning. Since the unlabeled data characterize the joint probability across diierent features, they could be used to boost weak classiiers by exploring discriminating features in a self-supervised fashion. Discriminant-EM (D-EM) attacks such problems by integrating discriminant analysis with the EM framework. Both linear and nonlinear methods are investigated in this paper. Based on kernel multiple discriminant analysis (KMDA), the nonlinear D-EM provides better ability to simplify the probabilistic structures of data distributions in a discrimination space. We also propose a novel data-sampling scheme for eecient learning of kernel discriminants. Our experimental results show that D-EM outperforms a variety of supervised and semi-supervised learning algorithms for many visual learning tasks, such as content-based image retrieval and invariant object recognition.