Two-dimensional signature of images and texture classification
Sheng Zhang, Guang Lin, Samy Tindel · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2022
We introduce a proper notion of two-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a two-dimensional object such as an image. It thus serves as a low-dimensional feature for pattern classification. Here, we implement a simple procedure for texture classification. In this context, we show that a low-dimensional set of features based on signatures produces an excellent accuracy.