351. Novel color image segmentation using self-generating prototypes
- Author
-
Zhiming Cui, Zhaohui Wang, Xiaohua Yuan, and Chunping Liu
- Subjects
Set (abstract data type) ,Learning vector quantization ,Property (programming) ,business.industry ,Color image ,Computer science ,Competitive learning ,Computer vision ,Image processing ,Artificial intelligence ,Image segmentation ,business ,Quantization (image processing) - Abstract
A new self-generating prototypes method based on SGNT is presented. This method uses reference patterns as initial prototype. This procedure can be implemented in a SGNT with specific architecture consisting of one root and the initial class number of reference patterns. The leaf in SGNT is defined with prototype vector, learning vector, center property vector and distant property vector. After training, prototype set are outputted. The main advantage of this method is that both the number of prototypes and their locations are learned from the training set without much human intervention. Experiments with synthesis and real color image the excellent performance of this classification scheme as compared to existing K-nearest neighbor (K-NN) and Learning vector quantization (LVQ) algorithm.
- Published
- 2007
- Full Text
- View/download PDF