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<p><it>Abstract</it>—Statistical clustering methods have long been used for a variety of signal 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 incorporating a Bayes risk term into the distortion measure used in the quantizer design algorithm. Once trained, the quantizer can operate to minimize 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 independent 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.</p>
Image compression, image classification, vector quantization, image coding, statistical clustering.

K. L. Oehler and R. M. Gray, "Combining Image Compression and Classification Using Vector Quantization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 17, no. , pp. 461-473, 1995.
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