Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert

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In this letter, a novel multiresolution algorithm for lossy gray-scale image compression is presented. High-quality low bit rate image compression is achieved first by segmenting an image into regions of different sizes based on perceptual variation in each region and then constructing a distinct code for each block by using the theory of projection pursuit (PP). Projection pursuit allows one to adaptively construct a better approximation for each block by optimally selecting basis functions. The process is stopped when the desired peak signal-to-noise ratio (PSNR) or bit rate (b/pixel) is achieved. At rates below 0.5 b/pixel, our algorithm shows superior performance, both in terms of PSNR and subjective image quality, over the Joint Photographers Expert Group (JPEG) algorithm [3], and comparable performance to the embedded zerotree wavelet (EZW) algorithm [11].

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In this paper a novel algorithm for adaptive signal expansion is presented. Here the main concern is to eficiently represent the natural images and audio signals. These signals are one or two dimensional signals with unknown or time-varying characteristics. For this type of signal, linear expansion with a fuced set of basis functions is not flexible enough to represent the data with the desired degree of sparseness. We introduce a new algorithm called Segmented Orthogonal Matching Pursuit (SOMP). Our experimental results show that the SOMP algorithm is more suitable than the existing signal expansion algorithms for eficient representation of audio and visual information.