论文标题
Contourcnn：用于轮廓数据分类的卷积神经网络
ContourCNN: convolutional neural network for contour data classification
论文作者
论文摘要
本文提出了一种新型的卷积神经网络模型，用于轮廓数据分析（CONTOURCNN）和形状分类。轮廓是代表封闭形状的点的圆序。为了处理轮廓表示的周期性特性，我们采用了圆形卷积层。轮廓通常是稀疏的。为了解决信息稀疏性，我们介绍了优先级池层，以根据其幅度选择功能。优先级的池层池功能低幅度，而其余的则保持不变。我们使用从EMNIST数据集提取的字母和数字形状评估了提出的模型，并获得了高分类精度。
This paper proposes a novel Convolutional Neural Network model for contour data analysis (ContourCNN) and shape classification. A contour is a circular sequence of points representing a closed shape. For handling the cyclical property of the contour representation, we employ circular convolution layers. Contours are often represented sparsely. To address information sparsity, we introduce priority pooling layers that select features based on their magnitudes. Priority pooling layers pool features with low magnitudes while leaving the rest unchanged. We evaluated the proposed model using letters and digits shapes extracted from the EMNIST dataset and obtained a high classification accuracy.
