We present a combination of an extended vector quantization (VQ) algorithm for training a speaker model and a gaussian interpretation of the VQ speaker model in the veri cation phase. This leads to a large decrease of the error rates compared to normal vector quantization and only a slight deterioration compared to full Gaussian mixture model (GMM) training. The training costs of the new method...

In this paper, a new technique for lossy compression of halftone images is proposed based on the vector quantization technique. A conventional vector quantization encoder is modified such that it embeds a block-based error diffusion process and takes a HVS model into account during the compression. This modification significantly improves the visual performance of encoded images while the compr...

| Entropy-constrained vector quantization (ECVQ) 3] offers substantially improved image quality over vector quan-tization (VQ) at the cost of additional encoding complexity. We extend results in the literature for fast nearest neighbor search of VQ to ECVQ. We use a new, easily computed distance that successfully eliminates most codewords from consideration.

Akfmct-An iterative descent algorithm based on a Lagrangian formulation is introduced for designing vector quantizers having minimum distortion subject to an entropy constraint. These entropy-constrained vector quantizers (ECVQ’s) can be used in tandem with variable rate noiseless coding systems to provide locally optimal variable rate block source coding with respect to a fidelity criterion. E...

Huge Volumes of detailed personal data is continuously collected and analyzed by different types of applications using data mining, analysing such data is beneficial to the application users. It is an important asset to application users like business organizations, governments for taking effective decisions. But analysing such data opens treats to privacy if not done properly. This work aims t...

The important point about VQ is that, we require reproduction vectors (instead of reproduction levels) that are known by the encpder and the decoder. The encoder takes an input vector, determines the best representing reproduction vector, and transmits the index of that vector. The decoder takes that index, and forms the reproduction vector because it already knows the reproduction vectors inst...

Learning Vector Quantization (LVQ) [1] is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) [2] is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ p...

This paper presents a novel method for stance and swing phase detection employing Learning Vector Quantization (LVQ), using knee angle information only. The results show detection accuracy of 95.9% in stance phase and 83.9% in swing phase. The research concludes an efficient replacement of footswitch for phase detection. The work can directly lead to low cost speed adaptive transtibial prosthes...

We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. The method is based on Hebbian learning and introduces weighting factors of the input dimensions which are automatically adapted to the speci c problem. The bene ts are twofold: On the one hand, the incorporation of relevance factors in the L...

In this paper, we propose video classification using linear manifolds (affine subspaces). In our method, we represent videos belonging to a same class by several linear manifolds using k-varieties clustering. When a test video is given, each frame of it votes for the class to which its nearest linear manifold belongs. According to this voting, the test video is classified into the class that ac...