Sparse image coding using learned overcomplete dictionaries

Images can be coded accurately using a sparse set of vectors from an overcomplete dictionary, with potential applications in image compression and feature selection for pattern recognition. We discuss algorithms that perform sparse coding and make three contributions. First, we compare our overcompl...

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Bibliographic Details
Published inProceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004 pp. 579 - 588
Main Authors Murray, J.F., Kreutz-Delgado, K.
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
New York NY
Subjects
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Bayesian methods
Computer science; control theory; systems
Detection, estimation, filtering, equalization, prediction
Dictionaries
Exact sciences and technology
Focusing
Image coding
Independent component analysis
Information, signal and communications theory
Matching pursuit algorithms
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Pixel
Signal and communications theory
Signal, noise
Telecommunications and information theory
Theoretical computing
Vectors
Bayes estimation
Dictionaries
Image processing
Data compression
Independent component analysis
Image compression
Pattern recognition
Variance
Image analysis
Image coding
Gaussian process
Sparse set
Greedy algorithm
Sparse representation
Learning algorithm
Artificial intelligence
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Summary:Images can be coded accurately using a sparse set of vectors from an overcomplete dictionary, with potential applications in image compression and feature selection for pattern recognition. We discuss algorithms that perform sparse coding and make three contributions. First, we compare our overcomplete dictionary learning algorithm (FOCUSS-CNDL) with overcomplete independent component analysis (ICA). Second, noting that once a dictionary has been learned in a given domain the problem becomes one of choosing the vectors to form an accurate, sparse representation, we compare a recently developed algorithm (sparse Bayesian learning with adjustable variance Gaussians) to well known methods of subset selection: matching pursuit and FOCUSS. Third, noting that in some cases it may be necessary to find a non-negative sparse coding, we present a modified version of the FOCUSS algorithm that can find such non-negative codings
ISBN:0780386084
0780386086
9780780386082
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2004.1423021