Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
This work provides a mathematically interpretable and faster alternative for image clustering, which could benefit researchers and practitioners seeking efficient and understandable methods.
This paper introduces Projected-Scattering Spectral Clustering (PSSC), a new image clustering algorithm that leverages the geometric structure of scattering transforms. PSSC achieves state-of-the-art performance among shallow clustering algorithms and comparable results to deep learning methods, while reducing execution time by over an order of magnitude.
In the last few years, large improvements in image clustering have been driven by the recent advances in deep learning. However, due to the architectural complexity of deep neural networks, there is no mathematical theory that explains the success of deep clustering techniques. In this work we introduce Projected-Scattering Spectral Clustering (PSSC), a state-of-the-art, stable, and fast algorithm for image clustering, which is also mathematically interpretable. PSSC includes a novel method to exploit the geometric structure of the scattering transform of small images. This method is inspired by the observation that, in the scattering transform domain, the subspaces formed by the eigenvectors corresponding to the few largest eigenvalues of the data matrices of individual classes are nearly shared among different classes. Therefore, projecting out those shared subspaces reduces the intra-class variability, substantially increasing the clustering performance. We call this method Projection onto Orthogonal Complement (POC). Our experiments demonstrate that PSSC obtains the best results among all shallow clustering algorithms. Moreover, it achieves comparable clustering performance to that of recent state-of-the-art clustering techniques, while reducing the execution time by more than one order of magnitude. In the spirit of reproducible research, we publish a high quality code repository along with the paper.