Unsupervised Method to Remove Noisy and Redundant Images in Scene Recognition

Mobile robotics has achieved important progress and level of maturity. Nevertheless, to increase the complexity of the tasks that mobile robots can perform in indoor environments, we need to provide them with a scene understanding of their surrounding. Scene recognition usually involves building image classifiers using training data. These classifiers work with features extracted from the images to recognize different categories. Later on, these classifiers can be used to label any image taken by the robot. The problem is that the training data used to recognize the scene might be redundant and noisy, thus reducing significantly the performance of the classifiers. To avoid this, we propose an unsupervised algorithm able to recognize when an image is unrepresentative, redundant or outlier. We have tested our algorithm in real and difficult environments achieving very promising results which take us a step closer to a complete unsupervised scene recognition with high accuracy.

keywords: Scene recognition, Canonical views, Unsupervised solution, Mobile robotics