Learning Object Groups for Scene Recognition

Abstract

Scene recognition is an important task for many computer vision and robotics applications. Recent progress in high-level object-based image representation has shown superior performance on scene classification tasks. In this work, we make an observation that groups of objects tend to co-occur frequently in a scene. We therefore propose to a novel framework that automatically learns object groups, and use them to build an image representation for scene recognition tasks. We model each object group as a template that explicitly encodes the spatial configurations of objects. To encourage the informativeness and discriminability, we learn the object group templates in a sparse filtering framework. Experiment results show that our object group representation could achieve state-of-the-art performance for both scene discovery and scene classification tasks.