Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
Topology representing networks
Neural Networks
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Gray-level reduction using local spatial features
Computer Vision and Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
A Hierarchic Method for Footprint Segmentation Based on SOM
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Hand gesture recognition using a neural network shape fitting technique
Engineering Applications of Artificial Intelligence
Exploiting a Growing Self-organizing Map for Adaptive and Efficient Color Quantization
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Color quantization using principal components for initialization of Kohonen Sofm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Multiresolution histogram analysis for color reduction
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Video summarization using a self-growing and self-organized neural gas network
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Engineering Applications of Artificial Intelligence
Golden retriever: a Java based open source image retrieval engine
Proceedings of the 21st ACM international conference on Multimedia
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
A genetic programming based system for the automatic construction of image filters
Integrated Computer-Aided Engineering
Hierarchical Color Quantization Based on Self-organization
Journal of Mathematical Imaging and Vision
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A new method for color reduction in a digital image is proposed, which is based on the development of a new neural network classifier and on a new method for Estimation of the Most Important Classes (EMIC). The proposed neural network combines the features of the well-known Growing Neural Gas (GNG) and the Kohonen Self-Organized Feature Map (KSOFM) neural networks. We call the new neural network Self-Growing and Self-Organized Neural Gas (SGONG). This combination produces a new neural network with outstanding features. The proposed technique utilizes the GNG mechanism of growing the neural lattice and the KSOFM leaning adaptation mechanism. Besides, introducing a number of criteria that have an effect on inserting or removing neurons, it is able to automatically define the number of the created neurons and their topology. Moreover, applying the EMIC method, the produced classes can be filtered and the most important classes can be found. The combination of SGONG and EMIC results in retaining the isolated and significant colors with the minimum number of color classes. The above techniques are able to be fed by both color and spatial features. For this reason a similarity function is used for vector comparison. The method is applicable to any type of color images and it can accommodate any type of color space.