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This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an established visual learning pipeline that has recently shown efficacy on the CIFAR-10 small image dataset. Experiments show that performance near batch oriented learning is achievable by combining naturally online learning mechanisms driven largely by stochastic gradient descent with strictly patch-wise operations. We further detail how back propagation can be used with a neural network classifier to modify parameters within the pipeline.