Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement

TL;DR

This paper introduces PSSC, a mathematically interpretable image clustering algorithm leveraging scattering transforms and orthogonal projections, achieving state-of-the-art results with high efficiency and reproducibility.

Contribution

The paper presents PSSC, a novel clustering method that exploits scattering transform geometry and orthogonal projections, offering interpretability and superior performance.

Findings

PSSC outperforms all shallow clustering algorithms.

PSSC achieves comparable results to deep learning methods.

PSSC reduces execution time by over tenfold.

Abstract

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.