Bayesian logistic regression with sparse general representation prior for multispectral image classification

Juan G. Serra; Pablo Ruiz; Rafael Molina; Aggelos K. Katsaggelos

doi:10.1109/ICIP.2016.7532687

Bayesian logistic regression with sparse general representation prior for multispectral image classification

Juan G. Serra, Pablo Ruiz, Rafael Molina, Aggelos K. Katsaggelos

Electrical and Computer Engineering

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

1 Scopus citations

Abstract

In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

Original language	English (US)
Title of host publication	2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	1893-1897
Number of pages	5
ISBN (Electronic)	9781467399616
DOIs	https://doi.org/10.1109/ICIP.2016.7532687
State	Published - Aug 3 2016
Event	23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2016-August
ISSN (Print)	1522-4880

Other

Other	23rd IEEE International Conference on Image Processing, ICIP 2016
Country/Territory	United States
City	Phoenix
Period	9/25/16 → 9/28/16

Funding

This work has been supported in part by the Ministerio de Economía y Competitividad under contract TIN2013-43880-R and the Department of Energy grant DE-NA0002520.

Keywords

Bayes Methods
Image Classification
Inference Algorithms

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2016.7532687

Cite this

Serra, J. G., Ruiz, P., Molina, R., & Katsaggelos, A. K. (2016). Bayesian logistic regression with sparse general representation prior for multispectral image classification. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 1893-1897). Article 7532687 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532687

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title = "Bayesian logistic regression with sparse general representation prior for multispectral image classification",

abstract = "In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.",

keywords = "Bayes Methods, Image Classification, Inference Algorithms",

author = "Serra, {Juan G.} and Pablo Ruiz and Rafael Molina and Katsaggelos, {Aggelos K.}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",

year = "2016",

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doi = "10.1109/ICIP.2016.7532687",

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Serra, JG, Ruiz, P, Molina, R & Katsaggelos, AK 2016, Bayesian logistic regression with sparse general representation prior for multispectral image classification. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings., 7532687, Proceedings - International Conference on Image Processing, ICIP, vol. 2016-August, IEEE Computer Society, pp. 1893-1897, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 9/25/16. https://doi.org/10.1109/ICIP.2016.7532687

Bayesian logistic regression with sparse general representation prior for multispectral image classification. / Serra, Juan G.; Ruiz, Pablo; Molina, Rafael et al.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. IEEE Computer Society, 2016. p. 1893-1897 7532687 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August).

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

TY - GEN

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N2 - In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

AB - In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

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Serra JG, Ruiz P, Molina R, Katsaggelos AK. Bayesian logistic regression with sparse general representation prior for multispectral image classification. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. IEEE Computer Society. 2016. p. 1893-1897. 7532687. (Proceedings - International Conference on Image Processing, ICIP). doi: 10.1109/ICIP.2016.7532687

Bayesian logistic regression with sparse general representation prior for multispectral image classification

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