NDLI: A finite mixture model based on pair-copula construction of multivariate distributions and its application to color image segmentation

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A finite mixture model based on pair-copula construction of multivariate distributions and its application to color image segmentation

Content Provider	ACM Digital Library
Author	Roy, Utpal Roy, Anandarup Parui, Swapan K.
Abstract	This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. The advantage of such a model is that the margins and the dependence structures are de-coupled from each other. Also, they could be modeled separately. In effect the mixture model (called DVMM) is capable of capturing a broader family of distributions including non-Gaussian models. As an application, we consider the task of color image segmentation in CIE-LUV color space. The process of segmentation could be viewed as an unsupervised clustering of the image pixels. The clusters usually represent the possible segments in the image. Here, the image pixels are assumed to be samples from a DVMM. The expectation maximization algorithm is used to estimate the model parameters. One cluster generally consists of several connected components inside an image. We further note the existence of redundant connected components that represent the boundary of two adjacent components. We here propose a methodology that merges such component pixels to the other two components. We conduct extensive experiments on Berkeley segmentation data set. We take a number of error measures to evaluate the quality of segmentation. On the basis of a comparison with two existing mixture model based segmentation approaches, we find our results encouraging.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450316606
DOI	10.1145/2425333.2425343
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-12-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Color image segmentation Mixture model D-vine Pair-copula construction
Content Type	Text
Resource Type	Article

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