Nicu Sebe | PubMed Health

PubMed2005

How to complete performance graphs in content-based image retrieval: add generality and normalize scope.

The performance of a Content-Based Image Retrieval (CBIR) system, presented in the form of Precision-Recall or Precision-Scope graphs, offers an incomplete overview of the system under study: The influence of the irrelevant items (embedding) is obscured. In this paper, we propose a comprehensive and well-normalized description of the ranking performance compared to the performance of an Ideal Retrieval System defined by ground-truth for a large number of predefined queries. We advocate normalization with respect to relevant class size and restriction to specific normalized scope values (the number of retrieved items). We also propose new three and two-dimensional performance graphs for total recall studies in a range of embeddings.

Algorithms↗

PubMed2004

Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction.

Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-computer interaction and pattern recognition: facial expression recognition and face detection.

Journal Article↗