In this paper, we have compared the performance of two recently developed vector quantization algorithms using different optimization criteria for clustering, namely, the adaptive fuzzy leader clustering (AFLC) , a neuro-fuzzy algorithm, and deterministic annealing (DA) , another unsupervised clustering algorithm based on probabilistic and statistical physics frameworks, with the rate distortion criterion as a performance measure. Such a comparison is useful for evaluating the efficiency of clustering algorithms for the purpose of image vector quantization instead of the conventional misclassification evaluation. This method is extended from analysis of image coding in the spatial domain to sample vectors in the wavelet domain with predictable distribution. These sample vectors possess multi-dimensional generalized Gaussian distribution through a new multi-scale feature extraction method. Our preliminary results show much improvement on reconstructed image quality over JPEG.
|Number of pages||6|
|State||Published - 2001|
|Event||International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States|
Duration: Jul 15 2001 → Jul 19 2001
|Conference||International Joint Conference on Neural Networks (IJCNN'01)|
|Period||07/15/01 → 07/19/01|