ECCV 2012 Short Course
Sparse Representation and Low-Rank Representation in Computer Vision
-- Theory, Algorithms, and Applications
The recent vibrant study of sparse representation and compressive sensing has led to numerous groundbreaking results in signal processing and machine learning. In this tutorial, we will present a series of three talks to provide a high-level overview about its theory, algorithms and broad applications to computer vision and pattern recognition. We will also point out ready-to-use MATLAB toolboxes available for participants to further acquire hands-on experience on these related topics.
Online Source Code and References:
Sparse Optimization: http://www.eecs.berkeley.edu/~yang/software/l1benchmark/index.html
This session introduces the basic concepts of
sparse representation and low-rank representation. The emphasis will be
on how to model and recover low-dimensional structures in
high-dimensional signals, and how to verify that the models are
appropriate. We will illustrate this process through examples drawn
from a number of vision applications. We will gently introduce the
foundational theoretical results in this area, and show how theory
informs the modeling process.
This session discusses several extensions of the basic sparse representation concept, from the original l-1 minimization formulation to group sparsity, Sparse PCA, Robust PCA, and compressive phase retrieval. These variations extend the applications of compressive sensing to multiple-view objection recognition, informative feature selection, and medical imaging. Efficient numerical algorithms are a focus of our discussion, which are responsible for recovering stable estimates of the sparse signals in high-dimensional space. Finally, we briefly discuss how to properly implement the sparsity minimization algorithms on modern many-core CPU/GPU environments.
This session extends the techniques
to enable the analysis of large batches of visual data. We will show
how tools and ideas from convex optimization give simple, robust
algorithms for recovering low-rank matrices from incomplete, corrupted
and noisy observations. Participants will learn how to identify
problems for which these tools may be appropriate, and how to apply
them effectively to solve practical problems such as robust batch image
alignment and the detection of symmetric structures in images. We will
illustrate the power and potential of these revolutionary tools in a
wide range of applications in computer visions including but not
limited to: Face and Text Recognition, Texture Repairing, Video
Panorama, Camera Calibration, Holistic Reconstruction of Urban Scenes,
etc. Finally, we will show generalizations to the problem of learning
sparse codes for large sets of visual data, give example applications.
Yi Ma is the principal researcher and manager of the Visual Computing Group at Microsoft Research Asia, Beijing. His research interests include multiple-view geometry, vision-based control, clustering and classification of high-dimensional data, estimation of hybrid models and systems. He is the author of the vision textbook "An Invitation to 3D Vision: From Images to Geometric Models". He is the recipient of the David Marr Prize, NSF Career Award, and the ONR Young Investigator Award. He was an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence from 2007 to 2011 and current the associate editors of the International Journal of Computer Vision and the new IMA Journal on Information and Inference. He has also served as the chief guest editors for special issues for the Proceedings of IEEE and the IEEE Signal Processing Magazine. He will serve as Program Chair for ICCV 2013 and General Chair for ICCV 2015.
is an Assistant Professor in the Electrical Engineering Department at
Columbia University. He received his PhD in Electrical Engineering from
the University of Illinois at Urbana-Champaign in October 2009, and was
with Microsoft Research from 2009-2011. His research is in the area of
high-dimensional data analysis. In particular, his recent research has
focused on developing algorithms for robustly recovering structured
signal representations from incomplete and corrupted observations, and
applying them to practical problems in imaging and vision. His work has
received an number of awards and honors, including the 2009
Lemelson-Illinois Prize for Innovation for his work on face
recognition, the 2009 UIUC Martin Award for Excellence in Graduate
Research, and a 2008-2010 Microsoft Research Fellowship.
Allen Y. Yang is a Research Scientist in the Department of EECS at UC Berkeley. He has also served as a consultant to several major companies and startups in IT industry. His primary research areas include pattern analysis of geometric and statistical models in very high-dimensional data spaces and applications in motion segmentation, image segmentation, face recognition, and signal processing in heterogeneous sensor networks. He has published three books/chapters, ten journal papers and more than 20 conference papers. He is also the inventor of three US patents. He received his BEng degree in Computer Science from the University of Science and Technology of China (USTC) in 2001. From the University of Illinois at Urbana-Champaign (UIUC), he received two MS degrees in Electrical Engineering and Mathematics in 2003 and 2005, respectively, and a PhD in Electrical and Computer Engineering in 2006. Among the awards he received are a Best Bachelor's Thesis Award from USTC in 2001, a Henry Ford II Scholar Award from UIUC in 2003, a Best Paper Award from the International Society of Information Fusion and a Best Student Paper Award from Asian Conference on Computer Vision in 2009.