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Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates

  • Jaspreet Singh KalsiEmail author
  • Muhammad Azam
  • Nizar Bouguila
Chapter
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

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 l1l2l3 color spaces. The results obtained from IBL mixture models (IBLMM) are more promising than ID mixture models (IDMM) and GID mixture models (GIDMM).

Keywords

Inverted Dirichlet distribution Generalized inverted Dirichlet Inverted Beta-Liouville Image segmentation Spatial information 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jaspreet Singh Kalsi
    • 1
    Email author
  • Muhammad Azam
    • 1
  • Nizar Bouguila
    • 2
  1. 1.Department of Electrical and Computer Engineering (ECE)Concordia UniversityMontrealCanada
  2. 2.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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