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Mixture Models and Applications pp 273–305Cite as

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

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Part of the book series: Unsupervised and Semi-Supervised Learning ((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 l 1 l 2 l 3 color spaces. The results obtained from IBL mixture models (IBLMM) are more promising than ID mixture models (IDMM) and GID mixture models (GIDMM).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering (ECE), Concordia University, Montreal, QC, Canada

    Jaspreet Singh Kalsi & Muhammad Azam

  2. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

Authors
  1. Jaspreet Singh Kalsi
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  2. Muhammad Azam
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  3. Nizar Bouguila
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Correspondence to Jaspreet Singh Kalsi .

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Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

  2. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

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Kalsi, J.S., Azam, M., Bouguila, N. (2020). Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates. In: Bouguila, N., Fan, W. (eds) Mixture Models and Applications. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-23876-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-23876-6_12

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