A novel automatic image segmentation method for Chinese literati paintings using multi-view fuzzy clustering technology

2019 ◽  
Vol 26 (1) ◽  
pp. 37-51 ◽  
Author(s):  
Jie Zhang ◽  
Yintao Zhou ◽  
Kaijian Xia ◽  
Yizhang Jiang ◽  
Yuan Liu
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Segmentation Method

scholarly journals Segmentasi Citra Ikan Tuna dengan Mahalanobis Histogram Thresholding dan Mahalanobis Fuzzy C-Means

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Andi Baso Kaswar ◽  
Agus Zainal Arifin ◽  
Arya Yudhi Wijaya
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Accuracy Improvement ◽  
Tuna Fish ◽  
Fuzzy C Means ◽  
Membership Value ◽  
Number Of Iterations

Abstract. Fuzzy C-Means segmentation algorithm based on Mahalanobis distance can be utilized to segment tuna fish image. However, initialization of pixels membership value and clusters centroid randomly cause the segmentation process become inefficient in terms of iteration and time of computation. This paper proposes a new method for tuna fish image segmentation by Mahalanobis Histogram Thresholding (M-HT) and Mahalanobis Fuzzy C-Means (MFCM). The proposed method consists of three main phases, namely: centroid initialization, pixel clustering and accuracy improvement. The experiment carried out obtained average of iteration amount is as many as 66 iteration with average of segmentation time as many as 162.03 second. While the average of Accuracy is 98.54%, average of Missclassification Error is 1.46%. The result shows that the proposed method can improve the efficiency of segmentation method in terms of number of iterations and time of segmentation. Besides that, the proposed method can give more accurate segmentation result compared with the conventional method.Keywords: Tuna Fish Image, Segmentation, Fuzzy Clustering, Histogram Thresholding, Mahalanobis Distance. Abstrak. Algoritma segmentasi Fuzzy C-Means berbasis jarak Mahalanobis dapat digunakan untuk mensegmentasi ikan tuna. Namun, inisialisasi derajat keanggotaan piksel dan centroid klaster secara random mengakibatkan proses segmentasi menjadi tidak efisien dalam hal iterasi dan waktu komputasi. Penelitian ini mengusulkan metode baru untuk segmentasi citra ikan tuna dengan Mahalanobis Histogram Thresholding (M-HT) dan Mahalanobis Fuzzy C-Means (MFCM). Metode ini terdiri atas tiga tahap utama, yaitu: inisialisasi centroid, pengklasteran piksel dan peningkatan akurasi. Berdasarkan hasil ekseprimen, diperoleh rata-rata jumlah iterasi sebanyak 66 iterasi dengan rata-rata waktu segmentasi 162,03 detik. Rata-rata Akurasi 98,54% dengan rata-rata tingkat Missclassification Error 1,46%. Hasil yang diperoleh menunjukkan bahwa metode yang diusulkan dapat meningkatkan efisiensi metode segmentasi dalam hal jumlah iterasi dan waktu segmentasi. Selain itu, metode yang diusulkan dapat memberikan hasil segmentasi yang lebih akurat dibandingkan dengan metode konvensional.Kata Kunci: Citra Ikan Tuna, Segmentasi, Fuzzy Clustering, Histogram Thresholding, Jarak Mahalanobis.

Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering

2010 ◽  
Vol 143-144 ◽  
pp. 379-383 ◽  
Author(s):  
Jing Zhang ◽  
Xiang Zhang ◽  
Jie Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Cluster Center ◽  
Adaptive Genetic Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
Advantages And Disadvantages ◽  
Initial Cluster ◽  
Important Means

Image segmentation is an important means of the implementation of image analysis. The existing segmentation methods have their own advantages and disadvantages in segmentation time and segmentation effect. Image segmentation based on fuzzy clustering and genetic algorithm is studied. An adaptive genetic algorithm is improved, the crossover rate and mutation rate are optimized, and a new adaptive operator is adopted to achieve a non-linear adaptive adjustment. A new combined image segmentation means is presented, in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. The practice proves that this image segmentation method and algorithm is superior to the traditional one, which improves the segmentation performance and the segmentation effect.

A Variational Level Set Remote Sensing SAR Image Segmentation Approach for Oil Spill Detecting Based on Fuzzy Cluster

2015 ◽  
Vol 719-720 ◽  
pp. 1049-1055 ◽  
Author(s):  
Jin Yu Liu ◽  
Zheng Ning Zhang ◽  
He Meng Yang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Oil Spill ◽  
Fuzzy Clustering ◽  
Sar Image ◽  
Segmentation Method ◽  
Level Set Function ◽  
Sar Image Segmentation ◽  
Variational Level Set ◽  
Set Function

Synthetic Aperture Radar (SAR) has become one of the important means for the ocean remote sensing detection of oil spills. The existing SAR image segmentation method has the issues of edge blur, poor contrast, non-uniform intensity image, so the segmentation effect is not ideal. This paper presents a variational level set SAR image of oil spill detection method based on fuzzy clustering. First of all, apply the threshold method on initial segmentation of the original SAR image to transform the initial segmented image as fuzzy clustering. Secondly, introduce the clustering results into the initial level set function to achieve the initial contour. Finally, add fuzzy clustering model in the level set energy function to complete the level set evolution process and get the final segmented image. This paper uses the threshold segmentation results to achieve the initialization of the variational level set function profile. In theory, it could improve the level set method for efficiency, and reduce the wrong segmentation phenomenon. The experimental results show that the SAR image segmentation method of oil spill has good segmentation qualities and is suitable for the edge complex image segmentation.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Sign in / Sign up

Export Citation Format

Share Document