A generalized multiclass histogram thresholding approach based on mixture modelling

作者：Boulmerka Aissa; Allili Mohand Said^*; Ait Aoudia Samy

来源：Pattern Recognition, 2014, 47(3): 1330-1348.

DOI：10.1016/j.patcog.2013.09.004

• Image segmentation
• Multi-modal class thresholding
• Mixture of generalized Gaussian distributions (MoGG)

This paper presents a new approach to multi-class thresholding-based segmentation. It considerably improves existing thresholding methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions using mixtures of generalized Gaussian distributions (MoGG). The proposed approach seamlessly: (1) extends the standard Otsu's method to arbitrary numbers of thresholds and (2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. MoGGs enable efficient representation of heavy-tailed data and multi-modal histograms with flat or sharply shaped peaks. Experiments on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques.

• 出版日期2014-3