Overlapped latent Dirichlet allocation for efficient image segmentation

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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.
Publisher
SPRINGER
Issue Date
2015-04
Language
English
Article Type
Article
Citation

SOFT COMPUTING, v.19, no.4, pp.829 - 838

ISSN
1432-7643
DOI
10.1007/s00500-014-1410-x
URI
http://hdl.handle.net/10203/198222
Appears in Collection
CS-Journal Papers(저널논문)
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