Graph regularized tensor sparse coding for image representation

Sparse coding (SC) is a unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, which destructs the intrinsic spatial structures of the images. In this paper, we propose a novel graph regularized tensor s...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Multimedia and Expo) pp. 67 - 72
Main Authors: Fei Jiang, Xiao-Yang Liu, Hongtao Lu, Ruimin Shen
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Computational modeling
Convolution
Dictionaries
Encoding
Image coding
Laplace equations
Manifold Learning
Sparse Coding
Tensile stress
Tensor Representation
Tensor-linear Combination
ISSN:1945-788X
Online Access:Get full text
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Summary:Sparse coding (SC) is a unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, which destructs the intrinsic spatial structures of the images. In this paper, we propose a novel graph regularized tensor sparse coding (GTSC) for image representation. GTSC preserves the local proximity of elementary structures in the image by adopting the newly proposed tubal-tensor representation. Simultaneously, it considers the intrinsic geometric properties by imposing graph regularization that has been successfully applied to uncover the geometric distribution for the image data. Moreover, the learned sparse representations by GTSC have better physical explanations as the key operation (i.e., circular convolution) in the tubal-tensor model preserves the shifting invariance property. Experimental results on image clustering demonstrate the effectiveness of the proposed scheme.
ISSN:1945-788X
DOI:10.1109/ICME.2017.8019522