2-d signature of images and texture classification
This work addresses texture classification, offering a novel feature extraction method, but it appears incremental as it adapts existing theory to a specific domain.
The authors tackled texture classification by introducing a 2-dimensional signature for images, inspired by rough paths theory, and achieved excellent accuracy with a low-dimensional feature set.
We introduce a proper notion of 2-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a 2-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.