Learning Functional Object-Categories from a Relational Spatio-Temporal Representation

Abstract. We propose a framework that learns functional objectes from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph that encodes qualitative spatio-temporal patterns of interaction between objects. Event classes are induced by statistical generalization, the instances of which encode similar patterns of spatio-temporal relationships between objects. Equivalence classes of objects are discovered on the basis of their similar role in multiple event instantiations. Objects are represented in a multidimensional space that captures their role in all the events. Unsupervised learning in this space results in functional object-categories. Experiments in the domain of food preparation suggest that our techniques represent a significant step in unsupervised learning of functional object categories from spatio-temporal patterns of object interaction.

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Muralikrishna Sridhar, Anthony G. Cohn, David C. Hogg
• Keywords: Artificial Intelligence, ECAI 2008, Qualitative Spatio-temporal Patterns, Spatio-temporal Data Sets, Spatio-temporal Patterns