Image classification by using associative noise-like coding memories

The main goal of this research is to exploit the noise insensitivity and the pattern-completion capability shown by associative memories in order to improve pattern classification in the presence of noisy input data. A stochastic analysis of the adopted associative model is performed: the estimated noise variance is identified as a discriminating parameter for classification, and the optimal threshold value, which minimizes error probability in the filtering and presentation of recalls, is determined. Such theoretical results make it possible to outline the basic structure of an associative recognition system, in which a set of modules perform error-variance measurements and yield a candidate for recognition. A closed-loop version of such a system is also provided so that iterative associative reading cycles may improve the quality of recalls. The system can also be supported by techniques that make its behavior shift and scale independent; special effort is devoted to achieving the maximum modularity of the overall structure. Binary-image classification is performed as a testing task, and experimental results that prove the accuracy of theoretical predictions are reported