Abstract

This paper proposes an interactive image segmentation method based on self-organizing maps (SOMs). In this paper, not only two dimensional SOMs but also higher dimensional SOMs are used for image segmentation. The proposed method was applied to actual images. The experimental results demonstrated that higher dimensional SOMs were able to achieve more accurate segmentation.

Highlights

  • IntroductionThe self-organizing maps (SOMs) is a type of artificial neural network that is composed of neural units arranged in a grid pattern

  • The concept of a self-organizing map (SOM) was proposed by T.Kohonen [1, 2]

  • The SOM is a type of artificial neural network that is composed of neural units arranged in a grid pattern

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Summary

Introduction

The SOM is a type of artificial neural network that is composed of neural units arranged in a grid pattern. The SOM can perform un-supervised classification for the input vectors. The similar input vectors are mapped near to each other in the SOM. SOMs have been used for data visualization [3], image segmentation [4, 5], and so forth. Many of these studies used SOMs whose units were arranged in two dimensional grid pattern. Such two dimensional SOMs were useful to visualize trends in data distribution. Higher dimensional SOMs are used to segment gray-scale and color images interactively, and the segmentation accuracy of these SOMs are compared with each other

High dimensional SOM
Interactive segmentation
Self organization
Sample 1
Conditions
Sample 2
Discussion
Conclusion
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