For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
A Spatially Correlated Mixture Model for Image Segmentation
Kosei KURISU Nobuo SUEMATSU Kazunori IWATA Akira HAYASHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2015/04/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
image segmentation, Gaussian processes, mixture models,
Full Text: PDF(923.1KB)>>
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.