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Color image segmentation using semi-bounded finite mixture models by incorporating mean templates. (English) Zbl 1434.62124
Bouguila, Nizar (ed.) et al., Mixture models and applications. Cham: Springer. Unsuperv. Semi-Superv. Learn., 273-305 (2020).
Summary: Finite mixture models (FMM) are very popular for image segmentation. But, FMM assumes that each pixel is independent from each other. Thus, it does not consider the spatial information of the pixels which makes FMM more sensitive to noise. Generally, the traditional FMM consists of prior probability (PP) and component conditional probability (CP). In this chapter, we have incorporated mean templates, namely weighted geometric mean template (WGMT) and weighted arithmetic mean template (WAMT) to compute the CP. For estimating PP, the weighted geometric mean prior probability (WGMPP) and weighted arithmetic mean prior probability (WAMPP) templates are used. Lastly, the Expectation-Maximization (EM) algorithm is used to estimate the hyper-parameters of the FMM. Our models are proposed based on inverted Dirichlet (ID), generalized inverted Dirichlet (GID), and inverted Beta-Liouville (IBL) mixture models using the mean templates. For experimentation, the Berkeley 500 (BSD500) and MIT’s Computational Visual Cognition Laboratory (CVCL) datasets are used. We have also employed eight image segmentation performance evaluation metrics such as adjusted Rand index and homogeneity score to validate the image segmentation results for the BSD500. Additionally, we have also compared the segmentation outputs for the CVCL dataset which are computed using the traditional RGB and $$l_1l_2l_3$$ color spaces. The results obtained from IBL mixture models (IBLMM) are more promising than ID mixture models (IDMM) and GID mixture models (GIDMM).
For the entire collection see [Zbl 1430.62012].
MSC:
 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62H35 Image analysis in multivariate analysis
BSDS; DeepLab
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