The Effect of Metric Space on the Results of Graph Based Colour Image Segmentation

Martina Zachariasova,Patrik Kamencay,Robert Hudec,Peter Lukac,Miroslav Benco,Slavomir Matuska

doi:10.26552/com.c.2012.3.68-72

Martina Zachariasova, Patrik Kamencay + Show 4 more

Open Access

https://doi.org/10.26552/com.c.2012.3.68-72

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Abstract

This paper deals with the impact of the metric space on the results of colour image segmentation algorithm. Distance and similarity measures are important tasks for quality of colour image segmentation. Main idea of this research is to make a comparison of algorithm results with using different metrics. Euclidean distance is the most used metric in many colour image segmentation algorithms. This paper shows comparison of this metric with many other metrics. Nine different metrics are gradually used in efficient graph based colour image segmentation algorithm created in the C++ language. The efficiency of precision and recall is one of the investigation tasks of colour image segmentation.

Highlights

Colour image segmentation is an important process in image analysis and processing
Efficient graph based colour image segmentation algorithm was used for the experiments
The main principle of this paper is to compare the results of segmentations with using different type of metrics

Summary

Introduction

Colour image segmentation is an important process in image analysis and processing. Image segmentation is fundamental problem in the image analysis. There is problem to find a fast, simple, automatic, robust algorithm that will be efficiently segment varied types of images [1], [2]. Represent pixels in the image, and the edges (vi, vj) ʦ E connect certain pairs of neighbouring nodes. Each edge (vi, vj) has responding weight w(vi, vj) that is nonnegative dissimilarity measure between connected nodes by the edge (e.g. the difference in colour, location, intensity, motion, etc). There are several techniques to correct pixels merging. One of most popular is merging pixels via their similarity or dissimilarity. Edges between two vertices in the same segment should have low weights and high weights for edges between two vertices in different segments [11]

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