Deep Image Feature Learning With Fuzzy Rules

Abstract

Feature extraction methods are key to many image processing tasks. At present, the most popular method is to use a deep neural network to automatically extract features through end-to-end training instead of the traditional hand-crafted feature extraction. However, the training of deep neural network relies heavily on data quality and quantity, and the network is a black-box model that has poor interpretability. Human intelligence can be leveraged here to improve the deep neural network model, where the human decision process can be integrated in feature learning and object classification to enhance robustness and interpretability. In this paper, the method Deep Image Feature Learning with Fuzzy Rules (DIFL-FR) is proposed, where human decision process is embedded in feature extraction by combining fuzzy logic rule-based modeling with deep-stacked learning strategy. The proposed method has the following distinctive characteristics. First, since the method is based on fuzzy sets and fuzzy inference, it can extract more robust features from noisy data scenes. Second, the method progressively learns the image features through a layer-by-layer approach based on fuzzy rules, so that the feature learning process can be better explained by the rules generated. Third, the learning process of the method has a high efficiency since it is only based on forward propagation without back propagation and iterative learning. Finally, while the method is based on unsupervised learning, it can be easily extended to supervised and semi-supervised learning cases. The results of extensive experiments conducted on image datasets of different scales clearly show the effectiveness of the proposed method.