Learning from sparse codes

In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image representation in the atom domain. We build on a structural difference function that permits to compare molecules and we derive an algorithm that analyses sparse codes and estimates the most relevant signal structure without reconstructing the images. Experiments on both synthetic and real image datasets confirm the benefits of our new method compared to traditional learning methods.


Published in:
2016 Ieee International Conference On Image Processing (Icip), 3862-3866
Presented at:
IEEE International Conference on Image Processing, September 25-28, 2016
Year:
2016
Publisher:
New York, Ieee
ISSN:
1522-4880
ISBN:
978-1-4673-9961-6
Keywords:
Laboratories:




 Record created 2016-08-16, last modified 2018-03-17

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