### Abstract

Sparse approximations that are evaluated using overcomplete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction. Incorporating shift invariance into sparse representation of images can improve sparsity while providing a good approximation. The K-SVD algorithm adapts the dictionary based on a set of training examples, without shift invariance constraints. This paper presents two algorithms for training dictionaries and evaluating shift-invariant sparse representations for image data. One is a modified version of the K-SVD algorithm and the other is a novel graph-based algorithm that adapts the dictionary and computes representations using a low complexity reconstruction procedure.

Original language | English (US) |
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Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |

Pages | 145-150 |

Number of pages | 6 |

DOIs | |

State | Published - 2008 |

Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008 |

### Other

Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
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Country | Mexico |

City | Cancun |

Period | 10/16/08 → 10/19/08 |

### Fingerprint

### ASJC Scopus subject areas

- Artificial Intelligence
- Software
- Electrical and Electronic Engineering

### Cite this

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008*(pp. 145-150). [4685470] https://doi.org/10.1109/MLSP.2008.4685470

**Shift-invariant sparse representation of images using learned dictionaries.** / Thiagarajan, Jayaraman J.; Ramamurthy, Karthikeyan N.; Spanias, Andreas.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008.*, 4685470, pp. 145-150, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 10/16/08. https://doi.org/10.1109/MLSP.2008.4685470

}

TY - GEN

T1 - Shift-invariant sparse representation of images using learned dictionaries

AU - Thiagarajan, Jayaraman J.

AU - Ramamurthy, Karthikeyan N.

AU - Spanias, Andreas

PY - 2008

Y1 - 2008

N2 - Sparse approximations that are evaluated using overcomplete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction. Incorporating shift invariance into sparse representation of images can improve sparsity while providing a good approximation. The K-SVD algorithm adapts the dictionary based on a set of training examples, without shift invariance constraints. This paper presents two algorithms for training dictionaries and evaluating shift-invariant sparse representations for image data. One is a modified version of the K-SVD algorithm and the other is a novel graph-based algorithm that adapts the dictionary and computes representations using a low complexity reconstruction procedure.

AB - Sparse approximations that are evaluated using overcomplete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction. Incorporating shift invariance into sparse representation of images can improve sparsity while providing a good approximation. The K-SVD algorithm adapts the dictionary based on a set of training examples, without shift invariance constraints. This paper presents two algorithms for training dictionaries and evaluating shift-invariant sparse representations for image data. One is a modified version of the K-SVD algorithm and the other is a novel graph-based algorithm that adapts the dictionary and computes representations using a low complexity reconstruction procedure.

UR - http://www.scopus.com/inward/record.url?scp=58049184130&partnerID=8YFLogxK

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U2 - 10.1109/MLSP.2008.4685470

DO - 10.1109/MLSP.2008.4685470

M3 - Conference contribution

SN - 9781424423767

SP - 145

EP - 150

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

ER -