Centroid Based Concept Learning for RGB-D Indoor Scene Classification

Classifying images taken from indoor scenes is an important area of research. The development of an accurate indoor scene classifier has the potential to improve indoor localization and decision-making for domestic robots, offer new applications for wearable computer users, and generally result in better vision-based situation awareness thus impacting a wide variety of applications. The introduction of deep learning methods, the creation of numerous large-scale datasets, and the development of specialized computing hardware have all contributed to the rapid improvement in image classification performance. One reason for deep learning's success has been the ability to learn multiple layers of generic image features that can then be used on other related computer vision problems. For instance, features from object trained image classifiers have been used to train indoor scene classifiers [27]. Yet, indoor scene classification is a challenging problem on its own.