KmsGC: An Unsupervised Color Image Segmentation Algorithm Based onK-Means Clustering and Graph Cut

Binmei Liang,Jianzhou Zhang

doi:10.1155/2014/464875

Abstract

For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combinesK-means clustering with graph cut. In the first stage,K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.

Highlights

Unsupervised color image segmentation plays a key role in various image processing and computer vision applications, such as medical image analysis [1], image retrieval [2], image editing [3], and pattern recognition [4]
Based on the work above, we present a new unsupervised color image segmentation approach which consists of two stages
We address the construction of energy function in our application as follows

Summary

Introduction

Unsupervised color image segmentation plays a key role in various image processing and computer vision applications, such as medical image analysis [1], image retrieval [2], image editing [3], and pattern recognition [4]. Watershed-based approaches [7, 8] use gradient information and morphological markers for the segmentation. Clusteringbased approaches [9, 10] capture the global characteristics of the images by calculating the image features, usually color or texture, to efficiently segregate data. In [9], the authors proposed a feature space analysis and clustering-based meanshift segmentation algorithm called MS algorithm. In [10], a CTM algorithm is presented, and it segments the images by clustering the texture features. Image Segmentation Based on Graph Cut. An image can be defined by a pair (P, I) consisting of a finite discrete set P ⊂ Zd (d > 0) of points (pixels in Z2, voxels in Z3, etc.) and a function I that maps each point p ∈ P to a value I(p) in some value space. The set of edges ε consists of all n-links and t-links

Objectives

Methods

Conclusion