Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images
Sunanda Mitra, Ramiro Castellanos, Su-Yu Yang, Surya Pemmaraju · Studies in fuzziness and soft computing · 2000
Image segmentation allows mapping of similar regions in a scene leading to recognition of distinct objects by high-level vision systems. Therefore an efficient technique of clustering is a natural choice for image segmentation. Recognition of similar patterns embedded in image data is the basis of clustering subregions in an image. Efforts to develop algorithms for adaptive and less computationally complex classification of data have led to implementation of statistical classifiers in artificial neural networks (of both supervised and unsupervised categories). Such neural network architectures are ways to achieve autonomous processing of patterns but are not considered to incorporate intelligent decision processes offered by various models of fuzzy clustering. Integration of fuzzy membership values of samples into neural network processing generates more powerful models for autonomous and intelligent pattern recognition algorithms. Efficient object extraction for image segmentation as well as vector quantization for image coding can be achieved by neuro-fuzzy clustering algorithms. Examples of both for noisy synthetic as well as natural gray-level/color images are demonstrated in the spatial domain and in the wavelet-transformed domain. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.