A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
A new neural network architecture based on adaptive resonance theory (ART) is proposed and applied to color image segmentation. A new mechanism of similarity measurement between patterns has been introduced to make sure that spatial information in feature space, including both magnitude and phase of input vector, has been taken into consideration. By these improvements, the new ART2 architecture is characterized by the advantages: (i) keeping the traits of classical ART2 network such as self-organizing learning, categorizing without need of the number of clusters, etc.; (ii) developing better performance in grouping clustering patterns; (iii) improving pattern-shifting problem of classical ART2. The new architecture is believed to achieve effective unsupervised segmentation of color image and it has been experimentally found to perform well in a modified L茂戮驴u茂戮驴v茂戮驴color space in which the perceptual color difference can be measured properly by spatial information.