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From High Definition Image to Low Space Optimization

Feigin, Micha and Feldman, Dan and Sochen, Nir (2012) From High Definition Image to Low Space Optimization. In: Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science. No.6667. Springer , Berlin, pp. 459-470. ISBN 978-3-642-24784-2.

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Signal and image processing have seen in the last few years an explosion of interest in a new form of signal/image characterization via the concept of sparsity with respect to a dictionary. An active field of research is dictionary learning: Given a large amount of example signals/images one would like to learn a dictionary with much fewer atoms than examples on one hand, and much more atoms than pixels on the other hand. The dictionary is constructed such that the examples are sparse on that dictionary i.e each image is a linear combination of small number of atoms. This paper suggests a new computational approach to the problem of dictionary learning. We show that smart non-uniform sampling, via the recently introduced method of coresets, achieves excellent results, with controlled deviation from the optimal dictionary. We represent dictionary learning for sparse representation of images as a geometric problem, and illustrate the coreset technique by using it together with the K–SVD method. Our simulations demonstrate gain factor of up to 60 in computational time with the same, and even better, performance. We also demonstrate our ability to perform computations on larger patches and high-definition images, where the traditional approach breaks down.

Item Type:Book Section
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Additional Information:© 2012 Springer-Verlag Berlin Heidelberg.
Subject Keywords:Sparsity; dictionary learning; K–SVD; coresets
Series Name:Lecture Notes in Computer Science
Issue or Number:6667
Record Number:CaltechAUTHORS:20200520-093348683
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103350
Deposited By: Tony Diaz
Deposited On:20 May 2020 16:42
Last Modified:16 Nov 2021 18:20

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