Fast and flexible convolutional sparse coding

Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole image...

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Published in:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5135 - 5143
Main Authors: Heide, Felix, Heidrich, Wolfgang, Wetzstein, Gordon
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2015
Subjects:
Classification
Coding
Computer vision
Conferences
Convergence
Convolution
Convolutional codes
Encoding
Image reconstruction
Learning
Linear systems
Optimization
Pattern recognition
Reconstruction
Tasks
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Abstract Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.
AbstractList Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.
Author Wetzstein, Gordon
Heide, Felix
Heidrich, Wolfgang
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  organization: Stanford Univ., Stanford, CA, USA
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Snippet Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and...
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SubjectTerms Classification
Coding
Computer vision
Conferences
Convergence
Convolution
Convolutional codes
Encoding
Image reconstruction
Learning
Linear systems
Optimization
Pattern recognition
Reconstruction
Tasks
Title Fast and flexible convolutional sparse coding
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