Supervised hyperspectral image segmentation: A convex formulation using hidden fields

Filipe Condessa, José Bioucas-Dias, Jelena Kovačević

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

Abstract

Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The optimization is obtained via integer optimization which is NP-hard, apart from few exceptions. We sidestep from the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with hyperspectral image segmentation.

Original languageEnglish (US)
Title of host publication2014 6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2014
PublisherIEEE Computer Society
ISBN (Electronic)9781467390125
DOIs
StatePublished - Jun 28 2014
Event6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 - Lausanne, Switzerland
Duration: Jun 24 2014Jun 27 2014

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2014-June
ISSN (Print)2158-6276

Other

Other6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
CountrySwitzerland
CityLausanne
Period6/24/146/27/14

Keywords

  • Constrained Split Augmented Lagrangian Shrinkage Algorithm (SALSA)
  • Image segmentation
  • alternating optimization
  • hidden Markov measure fields
  • hidden fields

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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