Image Data compression using vector quantization (VQ) has received a lot of attention in the recent years because of its optimality in rate distortion and adaptability. A fundamental goal of data compression is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. The combination of subband coding and vector quantization can provide a...

In this paper, we present new approaches to handle drift and shift in on-line data streams using evolving fuzzy systems (EFS), which are characterized by the fact that their structure is not fixed and not pre-determined. When dealing with drifts and shifts in data streams one needs to take into account two major issues: a) automatic detection of, and b) automatic reaction to this. To address th...

Digital audio has been ubiquitous over the past decade. Since it can be easily modified by editing tools, there has been a strong need to protect its content for secure multimedia applications. Previous audio authentication algorithms are mainly focused on either human speech or general audio with music as part of the test data, while special research on music authentication has been somewhat n...

We present a mixed-mode VLSI chip performing unsupervised clustering and classification, implementing models of Fuzzy Adaptive Resonance Theory (ART) and Learning Vector Quantization (LVQ), and extending to variants such as Kohonen Self-Organizing Maps (SOM). The parallel processor classifies analog vectorial data into a digital code in a single clock, and implements on-line learning of the ana...

The paper describes an approach to discover transparent fuzzy rules from data, which can be effectively used in fuzzy model-based medical diagnosis. The approach is based on three main stages. First, available symptoms measurements are clustered by our Crisp Double Clustering scheme, which identifies, in the first instance, informative prototypes in the original measurements space by a vector q...

The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaussian mixtures (GM) have been pointed out recently by Bezdek and Pal (1995). We extend this relation to their training, showing that learning rules by these models to estimate the cluster centers can be seen as approxim...