An Effective Texture Image Segmentation Approach and Parameter Selection Effects Based on Sparse Coding

2012 ◽  
Vol 6 (1) ◽  
pp. 441-444
Author(s):  
Lijuan Duan ◽  
Chunxia Ke ◽  
Zhen Yang ◽  
Jun Miao ◽  
Yuanhua Qiao
Keyword(s):  
Image Segmentation ◽  
Sparse Coding ◽  
Parameter Selection ◽  
Selection Effects ◽  
Texture Image ◽  
Texture Image Segmentation ◽  
Segmentation Approach

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

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