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A fully unsupervised color textured image segmentation algorithm using weighted mean histograms features

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Abstract

A new integrated feature distribution-based color textured image segmentation algorithm has been proposed. Two novel histogram-based inherent color texture feature extraction methods have been presented. From the histogram features, mean color texture histogram is calculated. Instead of concatenating the feature channels, a multichannel nonparametric Bayesean clustering is employed for primary segmentation. A region homogeneity-based merging algorithm is used for final segmentation. The proposed feature extraction techniques inherently combine color texture features rather then explicitly extracting it. Use of nonparametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about the number of color texture regions is required. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments using color textured and natural images. The experimental results reveal that superior segmentation results can be obtained through the proposed unsupervised segmentation framework.

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Authors and Affiliations

  1. Computer Science and Engineering Discipline, Khulna Unversity, Khulna, 9208, Bangladesh

    Md. Mahbubur Rahman

  2. Ex-faculty, School of Information Sciences, Tohoku University, Sendai, Japan

    Susumu Horiguchi

Authors
  1. Md. Mahbubur Rahman
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  2. Susumu Horiguchi
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Correspondence to Md. Mahbubur Rahman.

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Rahman, M.M., Horiguchi, S. A fully unsupervised color textured image segmentation algorithm using weighted mean histograms features. SIViP 6, 197–209 (2012). https://doi.org/10.1007/s11760-012-0289-1

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  • DOI: https://doi.org/10.1007/s11760-012-0289-1

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