Soft Computing

, Volume 19, Issue 4, pp 829–838 | Cite as

Overlapped latent Dirichlet allocation for efficient image segmentation

  • Young-Seob JeongEmail author
  • Ho-Jin Choi


Unsupervised methods for image segmentation have recently drawn significant attention because most images do not have labels or tags. A topic model is an unsupervised probabilistic method that captures the latent aspects of data, where each latent aspect or topic is associated with one homogeneous region. In this paper, we propose a new topic model for image segmentation task that incorporates spatial information into its structure based on the hypothesis that overlapped topic proportions convey spatial information. The model is efficient in time and memory, and we demonstrate this through comparison with other models using the MSRC image dataset.


Probabilistic topic model  Image segmentation Spatial information 



This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD060048AD).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceKAISTDaejeonSouth Korea

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