Combined compression and classification with learning vector quantization

Combined compression and classification with learning vector quantization

Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from aided target recognition (ATR), to medical diagnosis, to speech recognition, to fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector qu...

Combined compression and denoising of images using vector quantization

Compression of a noisy source is usually a two stage problem, involving the operations of estimation (denoising) and quantization. A survey of literature on this problem reveals that for the squared error distortion measure, the best possible compression strategy is to subject the noisy source to an optimal estimator followed by an optimal quantizer for the estimate. What we present in this pap...

Divergence-based classification in learning vector quantization

We discuss the use of divergences in dissimilarity based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study Divergence Based Learning Vector Quantization (DLVQ). We derive cost function based DLVQ schemes for the family...

Vector Quantization for Image Compression-Concept Quantization

Vector Quantization Based Image Compression

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate sub bands. These sub bands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. Lloyd extended scalar quantization technique is used to design memory less vect...