Image Segmentation Method Based On Finite Doubly Truncated Bivariate Gaussian Mixture Model with Hierarchical Clustering
Image segmentation is one of the most important area of image
retrieval. In colour image segmentation the feature vector of each
image region is ’n’ dimension different from grey level image. In
this paper a new image segmentation algorithm is developed and
analyzed using the finite mixture of doubly truncated bivariate
Gaussian distribution by integrating with the hierarchical
clustering. The number of image regions in the whole image is
determined using the hierarchical clustering algorithm. Assuming
that a bivariate feature vector (consisting of Hue angle and
Saturation) of each pixel in the image region follows a doubly
truncated bivariate Gaussian distribution, the segmentation
algorithm is developed. The model parameters are estimated using
EM-Algorithm, the updated equations of EM-Algorithm for a
finite mixture of doubly truncated Gaussian distribution are
derived. A segmentation algorithm for colour images is proposed
by using component maximum likelihood. The performance of the
developed algorithm is evaluated by carrying out experimentation
with five images taken form Berkeley image dataset and
computing the image segmentation metrics like, Global
Consistency Error (GCE), Variation of Information (VOI), and
Probability Rand Index (PRI). The experimentation results show
that this algorithm outperforms the existing image segmentation
algorithms.
Keywords: Image Segmentation, Finite Doubly Truncated Bivariate Gaussian distribution, Hierarchical Clustering, Image Quality Metrics, EM algorithm
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