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We present a new algorithm for matching pursuit (MP) dictionary design. This technique uses existing vector-quantization design techniques and an inner product-based distortion measure to learn functions from a set of training patterns. While this scheme can be applied to many MP applications, we focus on motion-compensated video coding. Given a set of training sequences, data are extracted from the high-energy packets of the motion-compensated frames. Dictionaries with different regions of support are trained, pruned, and finally evaluated on MPEG test sequences. We find that for high bit-rate QCIF sequences we can achieve improvements of up to 0.66 dB with respect to conventional MP with separable Gabor functions.