Abstract-Scene recognition is a highly valuable perceptual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in performance for the case of indoor scenes. We believe that this can be explained by the high appearance variability of indoor environments. This stresses the need to include highlevel semantic information in the recognition process. In this work we propose a new approach for indoor scene recognition based on a generative probabilistic hierarchical model that uses common objects as an intermediate semantic representation. Under this model, we use object classifiers to associate lowlevel visual features to objects, and at the same time, we use contextual relations to associate objects to scenes. As a further contribution, we improve the performance of current state-ofthe-art category-level object classifiers by including geometrical information obtained from a 3D range sensor that facilitates the implementation of a focus of attention mechanism within a Monte Carlo sampling scheme. We test our approach using real data, showing significant advantages with respect to previous state-of-the-art methods.
Espinace et al. REF presented an indoor scene recognition system based on a generative probabilistic hierarchical model using the contextual relations to associate objects to scenes.
Indoor scene recognition through object detection
2010 IEEE International Conference on Robotics and Automation
2010 IEEE International Conference on Robotics and Automation
Computer Science