Ensemble feature learning for material recognition with convolutional neural networks

Abstract

Material recognition is the process of recognizing the constituent material of the object, and it is a crucial step in many fields. Therefore, it is valuable to create a system that could achieve material recognition automatically. This paper proposes a novel approach named ensemble learning for material recognition with convolutional neural networks (CNNs). In the proposed method, firstly, a CNN model is trained to extract the image features. Secondly, knowledge-based classifiers are learned to get the probabilities of the test sample that belongs to different material categories. Finally, we propose three different ways to learn the ensemble features, which achieves higher recognition accuracy. The great difference from the prior work is that we combine the knowledge-based classifiers on probability level. Experimental results show that the proposed ensemble feature learning method performs better than the state-of-the-art material recognition methods and can archive a much higher recognition accuracy.