  Scene classification is a fundamental perception task for environmental
understanding in today's robotics. In this paper, we have attempted to exploit
the use of popular machine learning technique of deep learning to enhance scene
understanding, particularly in robotics applications. As scene images have
larger diversity than the iconic object images, it is more challenging for deep
learning methods to automatically learn features from scene images with less
samples. Inspired by human scene understanding based on object knowledge, we
address the problem of scene classification by encouraging deep neural networks
to incorporate object-level information. This is implemented with a
regularization of semantic segmentation. With only 5 thousand training images,
as opposed to 2.5 million images, we show the proposed deep architecture
achieves superior scene classification results to the state-of-the-art on a
publicly available SUN RGB-D dataset. In addition, performance of semantic
segmentation, the regularizer, also reaches a new record with refinement
derived from predicted scene labels. Finally, we apply our SUN RGB-D dataset
trained model to a mobile robot captured images to classify scenes in our
university demonstrating the generalization ability of the proposed algorithm.
