Multilevel thresholding using edge matching
Computer Vision, Graphics, and Image Processing
A segmentation algorithm for color images
Pattern Recognition Letters
A new approach for multilevel threshold selection
CVGIP: Graphical Models and Image Processing
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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Histogram Based Color Reduction through Self-Organized Neural Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Image Color Reduction Based on Self-Organizing Maps and Growing Self-Organizing Neural Networks
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Novel fast color reduction algorithm for time-constrained applications
Journal of Visual Communication and Image Representation
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern Recognition Letters
Two greedy tree growing algorithms for designing variable rate vector quantizers
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases the memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation, and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohonen Self-Organizing Map Neural Network is employed to form an adaptive color reduction method. To enhance the performance of this method, we have used redundant features obtained by one-to-one functions from three main components of the color image (e.g. Red, Green and Blue channels). Exploiting these features will increase the color discrimination and details illustration ability of the network compared to the conventional approaches. This method leads to satisfactory results in image segmentation, especially in small object detection problems. It is also investigated that if the number of features in Kohonen network grows even by using non-deterministic one-to-one functions, the network revenue considerably improves. Moreover, we will study the effect of various adaptation algorithms in Kohonen network training stage. Again, using a multi-stage color reduction procedure which employs both Kohonen neural networks and conventional vector quantization schemes improves the performance. Several experimental results are represented to illustrate the characteristics of different approaches.