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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages1893-1897
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • Bayes Methods
  • Image Classification
  • Inference Algorithms

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • 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). [7532687] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532687