Color image segmentation using a self-organizing map algorithm

2002 ◽  
Vol 11 (2) ◽  
pp. 136 ◽  
Author(s):  
Yung-Sheng Chen
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
Self Organizing

SELF–ORGANIZING MAP COMBINED WITH A FUZZY CLUSTERING FOR COLOR IMAGE SEGMENTATION OF EDIBLE BEANS

2003 ◽  
Vol 46 (3) ◽  
Author(s):  
Y. Chtioui ◽  
S. Panigrahi ◽  
L. F. Backer
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Map ◽  
Self Organizing

An Approach to Noisy Synthetic Color Image Segmentation Using Unsupervised Competitive Self-Organizing Map

2021 ◽  
pp. 227-238
Author(s):  
P. Ganesan ◽  
B. S. Sathish ◽  
L. M. I. Leo Joseph ◽  
B. Girirajan ◽  
P. Anuradha ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Map ◽  
Self Organizing

Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function

2016 ◽  
pp. 321-348 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Nsga Ii ◽  
Segmented Images ◽  
Single Objective ◽  
Self Organizing

A self-supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multilayer self-organizing neural network (MLSONN) architecture is proposed to segment multilevel gray scale images. In the same way, another NSGA-II based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self-organizing neural network (PSONN) architecture is purported to segment the color images in this article. These methods are intended to overcome the drawback of their single objective based counterparts. Three standard objective functions are employed as the multiple objective criteria of the NSGA-II algorithm to measure the quality of the segmented images.

Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

2019 ◽  
Vol 23 (1) ◽  
Author(s):  
J. M. Barrón Adame ◽  
M. S. Acosta Navarrete ◽  
J. Quintanilla Domínguez ◽  
R. Guzmán Cabrera ◽  
M. Cano Contreras ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Maps ◽  
Som Neural Network ◽  
Self Organizing

scholarly journals Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Dongxiang Chi
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Space ◽  
Saliency Map ◽  
Color Image Segmentation ◽  
Evaluation Index ◽  
Natural Image ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Computational Load

Natural image segmentation is an important topic in digital image processing, and it could be solved by clustering methods. We present in this paper an SOM-based k-means method (SOM-K) and a further saliency map-enhanced SOM-K method (SOM-KS). In SOM-K, pixel features of intensity and L∗u∗v∗ color space are trained with SOM and followed by a k-means method to cluster the prototype vectors, which are filtered with hits map. A variant of the proposed method, SOM-KS, adds a modified saliency map to improve the segmentation performance. Both SOM-K and SOM-KS segment the image with the guidance of an entropy evaluation index. Compared to SOM-K, SOM-KS makes a more precise segmentation in most cases by segmenting an image into a smaller number of regions. At the same time, the salient object of an image stands out, while other minor parts are restrained. The computational load of the proposed methods of SOM-K and SOM-KS are compared to J-image-based segmentation (JSEG) and k-means. Segmentation evaluations of SOM-K and SOM-KS with the entropy index are compared with JSEG and k-means. It is observed that SOM-K and SOM-KS, being an unsupervised method, can achieve better segmentation results with less computational load and no human intervention.

An Algorithm Based on Self-Organizing Kohonen’s SOM for Color Image Segmentation

2014 ◽  
Vol 926-930 ◽  
pp. 2942-2946
Author(s):  
Ting Ting Wang ◽  
Wei Dong
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Computational Method ◽  
Color Image Segmentation ◽  
Merging Process ◽  
Relationship Of ◽  
Self Organizing

The paper proposes an algorithm based on the self-organizing Kohonen’s SOM to resolve the difficulties brought by the information fusion in the color image segmentation. First, considering the relationship of NBS distance and human perception, the image’s information is transformed from the RGB to the Munsell color space. Combining the spatial information, the initial segmented regions are formed by the kohonen’s SOM training according to the computational method of distance provided in the paper. Second, the initial regions are merged until the termination rule of the merging process is contented. The algorithm syncretizes the color and spatial information, which is demonstrated that segmentation results hold favorable consistency in term of human perception consistency by a great deal of experiments.

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
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