Harnessing Sparse and Low-Dimensional Structures for Robust Clustering of Imagery Data

We propose a robust framework for clustering data. In practice, data obtained from real measurement devices can be incomplete, corrupted by gross errors, or not correspond to any assumed model. We show that, by properly harnessing the intrinsic low-dimensional structure of the data, these kinds of practical problems can be dealt with in a uniform fashion. In particular, we propose two robust segmentation algorithms: an algebraic method for data from multiple quadratic manifolds, and an information-theoretic approach for data from multiple linear subspaces. Our techniques draw from many diverse areas, including lossy data compression, sparse representation, algebraic geometry, and robust statistics. We verify the efficacy of our algorithms by applying them to the segmentation of tracked image features of objects in a dynamic scene under the affine and perspective camera models, and the segmentation of a natural image into regions with homogeneous texture. We benchmark the performance of our methods on many publicly available notion and imagery datasets. Our results are on par with state-of-the-art results, in many cases exceeding them. Finally, we explore potential extensions and improvements to our techniques as well as new applications.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Rao, Shankar Ramamohan

Contributors dc:contributor

• Ma, Yi

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3392444

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81149