COMBINING IMAGE COMPRESSION AND CLASSIFICATION USING VECTOR QUANTIZATION

Statistical clustering methods have long been used for a variety of si gnal processing applications,including both classification and vector quantization for signal compression. We describe a method of combining classification and compression into a single vector quantizer by inco rporating a Bayes risk term into the distortion measure used in the qu antizer design algorithm. Once trained, the quantizer can operate to m inimize the Bayes risk weighted distortion measure if there is a model providing the required posterior probabilities, or it can operate in a suboptimal fashion by minimizing only squared error. Comparisons are made with other vector quantizer based classifiers, including the ind ependent design of quantization and minimum Bayes risk classification and Kohonen's LVQ, A variety of examples demonstrate that the proposed method can provide classification ability close to or superior to LVQ while simultaneously providing superior compression performance.