scholarly journals Neutrosophic Set Transformation Matrix Factorization Based Active Contours for Color Texture Segmentation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 93887-93897 ◽  
Author(s):  
Yongsheng Dong ◽  
Hongyan Zhang ◽  
Zhonghua Liu ◽  
Chunlei Yang ◽  
Guo-Sen Xie ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Active Contours ◽  
Texture Segmentation ◽  
Transformation Matrix ◽  
Neutrosophic Set ◽  
Color Texture

scholarly journals Markov Random Field Based Color Texture Segmentation

2020 ◽  
Vol 8 (5) ◽  
pp. 1055-1060
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Gabor Filter ◽  
Wide Spectrum ◽  
Median Filter ◽  
Medical Image Analysis ◽  
Similarity Index ◽  
Texture Segmentation ◽  
Markov Random ◽  
Color Texture

Texture segmentation is one of the popular research domains and researchers across the globe are working on texture segmentation to enhance segmentation performance to address its requirements in many fields. Color texture segmentation has wide spectrum of applications in diverse fields such as segmentation of natural images, medical image analysis, remote sensing, shape extraction and inspection of products etc. This paper presents color texture segmentation algorithm which can satisfy requirements for such applications. Proposed algorithm is based on Markov Random Field (MRF) model eliminating the need of major contributor viz. Gabor filter used in past four decades for feature extraction and use only color as texture feature. Highly crude segmentation results are produced using only color as texture features. Crude segmentation results are enhanced by using Median filter with enlarged window size quantitatively determined by using parameters viz. structural similarity index (SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR). Feature space dimensions are reduced by factor of 11 in proposed approach and this reduced computations by a factor of 11. The experimentation is carried out on 80 multi-class color texture benchmark images from Prague texture segmentation dataset and 4 benchmark images in Vistex dataset. Mean segmentation accuracy achieved for Prague texture dataset is 87.55% and it is higher by 9.82% over the best performing algorithm among 11 state-of-art algorithms suggested in most recent literature. Accuracy achieved for Vistex dataset is 98.21%. Average SSIM for Prague dataset is 0.91403 and Vistex dataset is 0.9405.

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