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In this paper, we investigate how to incorporate spatial and/or temporal contextual information into classical histogram features with the aim of boosting visual classification performance. Firstly, we show that the stationary distribution derived from the normalized histogram-bin co-occurrence matrix characterizes the row sums of the original histogram-bin co-occurrence matrix. This underlying rationale of the histogram-bin co-occurrence features then motivates us to propose the concept of general contextualizing histogram process, which encodes the spatial and/or temporal contexts as local homogeneity distributions and produces the so called contextualized histograms by convoluting these local homogeneity distributions with the histogram-bin index images/videos. Finally, the third and even higher order contextualized histograms are instantiated for encoding more complicated and informative spatial and/or temporal contextual information into histograms. We evaluate these proposed methods on face recognition and group activity classification problems, and the results demonstrate that the contextualized histograms significantly boost the visual classification performance.