We discuss the problem of finding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple approximation method to the problem and we show experimental results using artificially generated signals. Furthermore,we use our approximation method to find sparse representations of classes of real signals, specifically of images of pedestrians. We discuss the relation between our formulation of the sparsity problem and the problem of finding representations of objects that are compact and appropriate for detection and classification
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel...
The topic of this thesis is sparse representations of signals. The thesis follows mainly the book Sp...
Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to sol...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
Sparse representation has been well investigated and discussed over the past decade due to its abili...
This dissertation studies two aspects of feature learning: representation learning and metric in fea...
In this paper, application of sparse representation (factorization) of signals over an overcomplete ...
Sparse representation with learning-based overcomplete dictionaries has recently achieved impressive...
This technical report combines two commonly-themed submissions to ICCV 2007. The two papers reconsid...
International audienceSparse representation has attracted much attention from researchers in fields ...
The works presented in this thesis focus on sparsity in the real world signals, its applications in ...
Representing signals as linear combinations of basis vectors sparsely selected from an overcom-plete...
Techniques from sparse signal representation are beginning to see significant impact in computer vis...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel...
The topic of this thesis is sparse representations of signals. The thesis follows mainly the book Sp...
Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to sol...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
Sparse representation has been well investigated and discussed over the past decade due to its abili...
This dissertation studies two aspects of feature learning: representation learning and metric in fea...
In this paper, application of sparse representation (factorization) of signals over an overcomplete ...
Sparse representation with learning-based overcomplete dictionaries has recently achieved impressive...
This technical report combines two commonly-themed submissions to ICCV 2007. The two papers reconsid...
International audienceSparse representation has attracted much attention from researchers in fields ...
The works presented in this thesis focus on sparsity in the real world signals, its applications in ...
Representing signals as linear combinations of basis vectors sparsely selected from an overcom-plete...
Techniques from sparse signal representation are beginning to see significant impact in computer vis...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
An overview is given of the role of the sparseness constraint in signal processing problems. It is s...
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel...