Citation: PATTERN RECOGNITION Volume: 37 Issue: 9 Pages: 1797-1807
Abstract: Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed.
A massively parallel architecture for a self organizing neural pattern recognition machine
A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture ... Article Details
Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system
A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations ... Article Details