Discrete Schroedinger Transform For Texture Recognition
This work addresses texture recognition for applications like plant species identification, but it is incremental as it applies a novel mathematical transform to an existing problem.
The authors tackled texture recognition by introducing a feature extraction method based on the discrete Schroedinger transform, which outperformed existing descriptors like textons and local binary patterns in classification accuracy across multiple benchmark databases and noise conditions.
This work presents a new procedure to extract features of grey-level texture images based on the discrete Schroedinger transform. This is a non-linear transform where the image is mapped as the initial probability distribution of a wave function and such distribution evolves in time following the Schroedinger equation from Quantum Mechanics. The features are provided by statistical moments of the distribution measured at different times. The proposed method is applied to the classification of three databases of textures used for benchmark and compared to other well-known texture descriptors in the literature, such as textons, local binary patterns, multifractals, among others. All of them are outperformed by the proposed method in terms of percentage of images correctly classified. The proposal is also applied to the identification of plant species using scanned images of leaves and again it outperforms other texture methods. A test with images affected by Gaussian and "salt \& pepper" noise is also carried out, also with the best performance achieved by the Schroedinger descriptors.