Graph Cut Based Unsupervised Color Image Segmentation

2012 ◽  
Author(s):  
Liang Bin-mei ◽  
Zhang Jian-zhou
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Color Image Segmentation

scholarly journals Optimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)

2019 ◽  
Vol 8 (4S2) ◽  
pp. 583-587
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Input Image ◽  
Color Image Segmentation ◽  
Weighted Constraints ◽  
Mutation Operators ◽  
Cut Method ◽  
Crossover And Mutation

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Binmei Liang ◽  
Jianzhou Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Weighted Graph ◽  
Minimum Cost ◽  
Graph Cut ◽  
Optimal Number ◽  
Color Image Segmentation ◽  
Compactness Criterion ◽  
Multiway Cut

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.

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation

Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

1997 ◽  
Vol 30 (7) ◽  
pp. 269-274
Author(s):  
R. Boussarsar ◽  
P. Martin ◽  
R. Lecordier ◽  
M. Ketata
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
Color Image Segmentation ◽  
Markov Random
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