This study presents a novel representation based on hierarchical histogram of local feature sequences for human interaction recognition. The authors' method basically combines the power of discriminative sequence mining and histogram representation for the effective recognition of human interactions. Our framework involves extracting visual features from the videos first, and then mining sequences of the visual features that occur consequently in space and time. After the mining step, we represent each video with a histogram pyramid of such sequences. We also propose to use soft clustering in the visual word construction step, such that more information-rich histograms can be obtained. The authors' experimental results on challenging human interaction recognition data sets indicate that the proposed algorithm performs on par with the state-of-the-art methods.