A Spatially Correlated Mixture Model for Image Segmentation

Kosei KURISU  Nobuo SUEMATSU  Kazunori IWATA  Akira HAYASHI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.4   pp.930-937
Publication Date: 2015/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDP7307
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
image segmentation,  Gaussian processes,  mixture models,  

Full Text: PDF(923.1KB)
>>Buy this Article

Summary: 
In image segmentation, finite mixture modeling has been widely used. In its simplest form, the spatial correlation among neighboring pixels is not taken into account, and its segmentation results can be largely deteriorated by noise in images. We propose a spatially correlated mixture model in which the mixing proportions of finite mixture models are governed by a set of underlying functions defined on the image space. The spatial correlation among pixels is introduced by putting a Gaussian process prior on the underlying functions. We can set the spatial correlation rather directly and flexibly by choosing the covariance function of the Gaussian process prior. The effectiveness of our model is demonstrated by experiments with synthetic and real images.