In order to consider the possibility that a binary code string observed from an image will transition to another binary code string, we introduce into the classifiers a transition likelihood model that predicts transitions based on quantization residual error.
This enables classifications from consideration of transitions to the originally desired binary code string, even if the observed binary code string differs from the genuinely desired binary code string due to some influence.
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