The methods of extracting image features are the key to many image processing tasks. At present, the most popular method is the deep neural network which can automatically extract robust features through end-to-end training instead of hand-crafted feature extraction. However, the deep neural network currently faces many challenges: 1) its effectiveness is heavily dependent on large datasets, so the computational complexity is very high; 2) it is usually regarded as a black box model with poor interpretability. To meet the above challenges, a more interpretable and scalable feature learning method, i.e., deep image feature learning with fuzzy rules (DIFL-FR), is proposed in the paper, which combines the rule-based fuzzy modeling technique and the deep stacked learning strategy. The method progressively learns image features through a layer-by-layer manner based on fuzzy rules, so the feature learning process can be better explained by the generated rules. More importantly, the learning process of the method is only based on forward propagation without back propagation and iterative learning, which results in the high learning efficiency. In addition, the method is under the settings of unsupervised learning and can be easily extended to scenes of supervised and semi-supervised learning. Extensive experiments are conducted on image datasets of different scales. The results obviously show the effectiveness of the proposed method.

翻译：图像特征提取方法是许多图像处理任务的关键。目前最流行的方法是深度神经网络，它能通过端到端训练自动提取鲁棒特征，而无需人工设计特征提取。然而，深度神经网络当前面临诸多挑战：1）其有效性高度依赖于大规模数据集，导致计算复杂度极高；2）它通常被视为可解释性较差的"黑箱"模型。为应对上述挑战，本文提出一种更具可解释性和可扩展性的特征学习方法——基于模糊规则的深度图像特征学习（DIFL-FR），该方法融合了基于规则的模糊建模技术与深度堆叠学习策略。该方法通过基于模糊规则的逐层方式渐进学习图像特征，使得特征学习过程能够通过生成的规则得到更好解释。更重要的是，该方法的学习过程仅基于前向传播，无需反向传播和迭代学习，从而具有较高的学习效率。此外，该方法基于无监督学习设置，并可轻松扩展到监督学习和半监督学习场景。我们在不同规模的图像数据集上进行了大量实验，结果清晰验证了所提方法的有效性。