Abstract
The topic of achieving rotational invariance in convolutional neural networks (CNNs) has gained considerable attention recently, as this invariance is crucial for many computer vision tasks such as image classification and matching. In this letter, we propose a Sorting Convolution (SC) inspired by some hand-crafted features of texture images, which achieves continuous rotational invariance without requiring additional learnable parameters or data augmentation. Further, SC can directly replace the conventional convolution operations in a classic CNN model to achieve its rotational invariance. Based on MNIST-rot dataset, we first analyze the impact of convolutional kernel sizes, different sampling and sorting strategies on SC's rotational invariance, and compare our method with previous rotation-invariant CNN models. Then, we combine SC with VGG, ResNet and DenseNet, and conduct classification experiments on popular texture and remote sensing image datasets. Our results demonstrate that SC achieves the best performance in the aforementioned tasks.
Abstract (translated)
在卷积神经网络(CNN)中实现旋转不变的主题最近吸引了相当大的关注，因为这对于许多计算机视觉任务，如图像分类和匹配，是至关重要的。在本信中，我们提出了一种基于纹理图像手工特征的排序卷积(SC)，该卷积实现了连续的旋转不变性，而不需要额外的可学习参数或数据增强。此外，SC可以直接在经典CNN模型中替代传统的卷积操作来实现其旋转不变性。基于MNIST-rot数据集，我们首先分析了卷积内核大小、不同采样和排序策略对SC的旋转不变的影响，并比较了我们的方法与其他旋转不变的CNN模型。随后，我们结合SC与VGG、ResNet和DenseNet，在流行的纹理和遥感图像数据集上开展分类实验。我们的结果表明，SC在这些任务中取得了最佳表现。
URL
https://arxiv.org/abs/2305.14462
