Recognizing Scenes by Simulating Implied Social Interaction Networks

Indoor scene recognition remains a challenging problem for autonomous systems. Recognizing public spaces (e.g., libraries, classrooms), which contain collections of commonplace objects (e.g., chairs, tables), is particularly vexing; different furniture arrangements imply different types of social interaction, hence different scene labels. If people arrange rooms to support social interactions of one type or another, then object relationships that reflect the general notion of social immediacy may resolve some of the ambiguity encountered during scene recognition. We thus describe an approach to indoor scene recognition that uses the context provided by inferred social affordances as input to a hybrid cognitive architecture (ACT-R) that can represent, apply and learn knowledge relevant to classifying scenes. To provide common ground, we demonstrate how sub-symbolic learning processes in ACT-R, which plausibly give rise to human cognition, can mimic the performance of a simple, widely used machine learning technique (k-nearest neighbor classification).