On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks

Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.

Automated summary

Unsupervised image segmentation is an important task in computer vision systems, and a mixture model can be used to obtain segmented images. However, problems arise when the optimal mixture model contains a large number of components. This paper investigates methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation.