Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images.

Wutthichai Phornphatcharaphong,Nawapak Eua-Anant

doi:10.3390/jimaging6070072

Wutthichai Phornphatcharaphong, Nawapak Eua-Anant

Open Access

https://doi.org/10.3390/jimaging6070072

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Journal: Journal of imaging	Publication Date: Jul 16, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Khon Kaen University

Abstract

This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.

Highlights

In digital image processing, image segmentation that reduces the amount of unnecessary data and preserves the important information needed for analysis plays an important role in image analysis
This paper introduces an edge-based classical image segmentation algorithm for a color image using particle motion in a vector image field derived from local color distance images (PMLCD)
The concept of using two such orthogonal vector fields for boundary extraction in a grayscale image was introduced in the particle motion in a vector image field (PMVIF) algorithm, where the gradient–Laplacian vector field used as a normal compressive vector field and the Hamiltonian gradient vector field used as an edge vector field are given as follows:

Summary

Introduction

Image segmentation that reduces the amount of unnecessary data and preserves the important information needed for analysis plays an important role in image analysis. This section describes the principle of a traditional boundary extraction algorithm based on particle motion in a vector image field (PMVIF), which is an edge-based classical image segmentation approach. In a discretized image where a set of pixels or voxels is the only class that can exist, explicit representations of region boundaries, such as a curve or a surface, are difficult to encode. In this case, a normal compressive vector field [21,22,23], where all vectors are normal and point to the nearest interface, providing information about the direction to the nearest boundary, is more suitable to be used as an implicit boundary representation. The concept of using two such orthogonal vector fields for boundary extraction in a grayscale image was introduced in the PMVIF algorithm, where the gradient–Laplacian vector field used as a normal compressive vector field and the Hamiltonian gradient vector field used as an edge vector field are given as follows:

Methods

Results

Conclusion