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Accurate image segmentation using Gaussian mixture model with saliency map

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Abstract

Gaussian mixture model (GMM) is a flexible tool for image segmentation and image classification. However, one main limitation of GMM is that it does not consider spatial information. Some authors introduced global spatial information from neighbor pixels into GMM without taking the image content into account. The technique of saliency map, which is based on the human visual system, enhances the image regions with high perceptive information. In this paper, we propose a new model, which incorporates the image content-based spatial information extracted from saliency map into the conventional GMM. The proposed method has several advantages: It is easy to implement into the expectation–maximization algorithm for parameters estimation, and therefore, there is only little impact in computational cost. Experimental results performed on the public Berkeley database show that the proposed method outperforms the state-of-the-art methods in terms of accuracy and computational time.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grants 61271312, 61201344, 61401085, 31571001 and 81530060; by Natural Science Foundation of Jiangsu Province under Grants BK2012329, BK2012743, BK20150647, DZXX-031, BY2014127-11; by the ‘333’ Project under Grant BRA2015288; and by the Qing Lan Project.

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Authors and Affiliations

  1. Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China

    Hui Bi, Hui Tang, Guanyu Yang & Huazhong Shu

  2. Key Laboratory of Computer Network and Information Integration, Ministry of Education, Nanjing, China

    Hui Bi, Hui Tang, Guanyu Yang & Huazhong Shu

  3. Centre de Recherche en Information Biomédicale sino-français (CRIBs), Nanjing, China

    Huazhong Shu & Jean-Louis Dillenseger

  4. INSERM U1099, 35000, Rennes, France

    Jean-Louis Dillenseger

  5. Laboratoire Traitement du Signal et de l’Image, Université de Rennes I, 35000, Rennes, France

    Jean-Louis Dillenseger

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  1. Hui Bi
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  2. Hui Tang
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  3. Guanyu Yang
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  4. Huazhong Shu
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  5. Jean-Louis Dillenseger
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Correspondence to Hui Bi.

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Bi, H., Tang, H., Yang, G. et al. Accurate image segmentation using Gaussian mixture model with saliency map. Pattern Anal Applic 21, 869–878 (2018). https://doi.org/10.1007/s10044-017-0672-1

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