Segmentation of Texture Image by Combining Multiple Segmentation Results

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  • LIU Guoxiang
    Department of Information Science and Intelligent Systems, Faculty of Engineering, the University of Tokushima
  • OE Shunichiro
    Department of Information Science and Intelligent Systems, Faculty of Engineering, the University of Tokushima

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Abstract

This paper presents a Cellular Neural Network (CNN)-based algorithm to segment a texture image by combining some texture segmentation results. Due to the diversity of texture, using multiple segmentation results segmented by different algorithms is necessary for texture image segmentation problems. In this paper, a new method called Composition-Combination is proposed to combine some initial segmentation results. A new kind of CNN called Multi-objective CNN (MOCNN) is developed to improve the combination result of Composition-Combination and produce final segmentation. Different from the standard CNN, each cell of MOCNN has multiple vectors denote different features of cell, and one vector will occupy the cell against other vectors when the network gets to the equilibrium state.

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