An image classification algorithm based on the topological features

The traditional content-based image feature extraction method only focuses on the visual features at the bottom of the image, mainly extracting color features, texture features, shape features, and local features, without considering the spatial information of the image. This article proposes an image classification algorithm based on topological feature extraction, which fully considers the spatial position relationship and topological invariance of images. Firstly, the image is binarized and then filtered by a filtering function to obtain a grayscale image. The persistent image is calculated and further transformed into topological feature descriptors. Finally, machine learning algorithms are used for classification. Experiments conducted on the MNIST dataset showed that the enhanced classification algorithm, which incorporates spatial topological features of images, not only reduces the algorithm's complexity but also maintains classification accuracy.