Learning to Label Images

  • Richard Zemel | University of Toronto

The problem of image labeling, in which each pixel is assigned to one of a finite set of labels, is a difficult problem, as it entails deciding which components of an image belong to the same object as well as classifying the components. I will describe two approaches we have taken to this problem, both utilizing conditional random fields to model contextual effects. The first uses a novel form of learning higher-order structure, which we developed for this work but has broader applicability. The second is a simpler and more efficient method that turns out to work just as well. In both cases, the model is trained on a database of images and the learning method estimates model parameters by maximizing a lower bound of the data likelihood. We examine performance on three real-world image databases, and compare our system to a standard classifier and other conditional random field approaches.

Speaker Details

Rich Zemel is an Associate Professor in the Department of Computer Science at the University of Toronto. His academic career has included stops at: Harvard, Salk Institute, CMU, Arizona, Princeton, and Toronto. His research interests cover a range of topics in machine learning, computer vision, and neural coding. Specific interests include unsupervised learning, boosting, object recognition and motion analysis, collaborative filtering, and probabilistic models of neural representations.

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