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An analysis-synthesis loop model using kernel method

An analysis-synthesis loop model is constructed in feature space into which an input vector is mapped by a particular non-linear function. In this model, the recognition process is realized by comparison of the mapped input vector to reconstructed vectors that are generated initially and refined iteratively by a linear combination of the basis vectors in feature space. The kernel method allows efficient computation of the analysis-synthesis loop in the high dimensional feature space. Some experiments based on our model show more effective recognition of real-world images than that based on the linear model.