A Novel Two-Dimensional Tensor Sparse Coding Algorithm for Image Representation

Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning. However, conventional sparse coding vectorizes the input images, which breaks apart the local proximity of pixels and destructs the elementary objects of images. In this paper,...

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Bibliographic Details
Published in:Procedia computer science Vol. 131; pp. 234 - 242
Main Authors: Lu, Jiaren, Jiang, Fei, Shen, Ruimin
Format: Journal Article
Language:English
Published: Elsevier B.V 2018
Subjects:
Sparse Coding
Tensor-linear Combination
Sparse Coding
Tensor-linear Combination
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning. However, conventional sparse coding vectorizes the input images, which breaks apart the local proximity of pixels and destructs the elementary objects of images. In this paper, we propose a novel two-dimensional sparse coding (2DSC) model that represents gray images as the tensor-linear combinations under a novel algebraic framework. 2DSC learns much more concise dictionaries because of the circular convolution operator, since the shifted versions of the learned atoms by conventional SC is treated the same. We apply 2DSC to natural images and demonstrate that 2DSC returns meaningful dictionaries for large patches, which is not true for conventional SC.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.04.208