Feature engineering continues to play a critical role in image classification, particularly when interpretability and computational efficiency are prioritized over deep learning models with millions of parameters. In this study, we revisit classical machine learning based image classification through a novel approach centered on Permutation Entropy (PE), a robust and computationally lightweight measure traditionally used in time series analysis but rarely applied to image data. We extend PE to two-dimensional images and propose a multiscale, multi-orientation entropy-based feature extraction approach that characterizes spatial order and complexity along rows, columns, diagonals, anti-diagonals, and local patches of the image. To enhance the discriminatory power of the entropy features, we integrate two classic image descriptors: the Histogram of Oriented Gradients (HOG) to capture shape and edge structure, and Local Binary Patterns (LBP) to encode micro-texture of an image. The resulting hand-crafted feature set, comprising of 780 dimensions, is used to train Support Vector Machine (SVM) classifiers optimized through grid search. The proposed approach is evaluated on multiple benchmark datasets, including Fashion-MNIST, KMNIST, EMNIST, and CIFAR-10, where it delivers competitive classification performance without relying on deep architectures. Our results demonstrate that the fusion of PE with HOG and LBP provides a compact, interpretable, and effective alternative to computationally expensive and limited interpretable deep learning models. This shows a potential of entropy-based descriptors in image classification and contributes a lightweight and generalizable solution to interpretable machine learning in image classification and computer vision.

翻译：特征工程在图像分类中持续发挥着关键作用，尤其是在可解释性与计算效率优先于数百万参数的深度学习模型时。本研究通过一种以排列熵（PE）为核心的新方法，重新审视基于经典机器学习的图像分类。PE是一种稳健且计算轻量的度量，传统上用于时间序列分析，但很少应用于图像数据。我们将PE扩展至二维图像，并提出一种多尺度、多方向的基于熵的特征提取方法，该方法沿图像的行、列、对角线、反对角线以及局部图像块来刻画空间秩序与复杂性。为增强熵特征的判别能力，我们整合了两种经典的图像描述符：用于捕捉形状与边缘结构的定向梯度直方图（HOG），以及用于编码图像微观纹理的局部二值模式（LBP）。由此生成的手工特征集包含780个维度，用于训练通过网格搜索优化的支持向量机（SVM）分类器。所提方法在多个基准数据集上进行了评估，包括Fashion-MNIST、KMNIST、EMNIST和CIFAR-10，结果表明，在不依赖深度架构的情况下，该方法实现了具有竞争力的分类性能。我们的结果证明，PE与HOG和LBP的融合提供了一种紧凑、可解释且有效的替代方案，以应对计算成本高昂且可解释性有限的深度学习模型。这展示了基于熵的描述符在图像分类中的潜力，并为图像分类与计算机视觉中可解释的机器学习贡献了一种轻量且可泛化的解决方案。