1 research outputs found
Neither Global Nor Local: A Hierarchical Robust Subspace Clustering For Image Data
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