EK-SVD: Optimized dictionary design for sparse representations

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EK-SVD: Optimized dictionary design for sparse representations
Sparse representations using overcomplete dictionaries are used in a variety of field such as pattern recognition and compression. However, the size of dictionary is usually a tradeoff between approximation speed and accuracy. In this paper we propose a novel technique called the Enhanced K-SVD algorithm (EK-SVD), which finds a dictionary of optimized sizefor a given dataset, without compromising its approximation accuracy. EK-SVD improves the K-SVD dictionary learning algorithm by introducing an optimized dictionary size discovery feature to K-SVD. Optimizing strict sparsity and MSE constraints, it starts with a large number of dictionary elements and gradually prunes the under-utilized or similar-looking elements to produce a well-trained dictionary that has no redundant elements. Experimental results show the optimized dictionaries learned using EK-SVD give the same accuracy as dictionaries learned using the K-SVD algorithm while substantially reducing the dictionary size by 60%.
Raazia Mazhar, Paul D. Gader
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Raazia Mazhar, Paul D. Gader
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