Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
Abstract
In this paper, we consider the problem of subspace clustering in presence of contiguous noise, occlusion and disguise. We argue that self-expressive representation of data in current state-of-the-art approaches is severely sensitive to occlusions and complex real-world noises. To alleviate this problem, we propose a hierarchical framework that brings robustness of local patches-based representations and discriminant property of global representations together. This approach consists of 1) a top-down stage, in which the input data is subject to repeated division to smaller patches and 2) a bottom-up stage, in which the low rank embedding of local patches in field of view of a corresponding patch in upper level are merged on a Grassmann manifold. This summarized information provides two key information for the corresponding patch on the upper level: cannot-links and recommended-links. This information is employed for computing a self-expressive representation of each patch at upper levels using a weighted sparse group lasso optimization problem. Numerical results on several real data sets confirm the efficiency of our approach.
Cite
@article{arxiv.1905.07220,
title = {Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data},
author = {Maryam Abdolali and Mohammad Rahmati},
journal= {arXiv preprint arXiv:1905.07220},
year = {2020}
}