Paper Info
Title
Histogram-based image segmentation using variational mode decomposition and correlation coefficients.
Abstract
Image segmentation is defined by separating scenes into different parts. The grayscale histogram-based method is a widely used method in image segmentation because of its low computational complexity. However, traditional grayscale histogram-based methods usually suffer from wrong segmentation or low structure because of the irregularities and sharp details on the grayscale histogram and the absence of spatial information. This study presents an accurate image segmentation method by using the variational mode decomposition (VMD) and correlation coefficient (CC). First, grayscale histogram is decomposed into certain band-limit modes by VMD to remove the adverse effects. Then, the candidate centroids of the clusters are obtained by searching the refined histogram both vertically and horizontally. The K-means method is used to generate the segmentation results. Finally, some over-segmented regions are merged following the CCs of spatial histogram, which is also refined by the VMD method. The proposed method was applied to four images. The obtained experimental results show that the proposed method can generate reasonable segmentation results than those of the existing image segmentation algorithms.
Year
DOI
Venue
2017
10.1007/s11760-017-1101-z
Signal, Image and Video Processing
Keywords
Field
DocType
Image segmentation, Variational mode decomposition, k-means, histogram, Correlation coefficient
Computer vision,Scale-space segmentation,Pattern recognition,Histogram matching,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Balanced histogram thresholding,Image histogram,Histogram equalization,Mathematics
Journal
Volume
Issue
ISSN
11
8
1863-1703
Citations 
PageRank 
References 
2
0.40
14
Authors
3
Name
Order
Citations
PageRank
Duo Hao120.40
qiuming li291.53
Chengwei Li3113.61