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Recent commodity depth cameras have been widely used in the applications of video games, business, surveillance and have dramatically changed the way of human-computer interaction. They provide rich multimodal information that can be used to interpret the human-centric environment. However, it is still of great challenge to model the temporal dynamics of the human actions and great potential can be exploited to further enhance the retrieval accuracy by adequately modeling the patterns of these actions. To address this challenge, we propose a temporal order-preserving dynamic quantization method to extract the most discriminative patterns of the action sequence. We further present a multimodal feature fusion method that can be derived in this dynamic quantization framework to exploit different discriminative capability of features from multiple modalities. Experiments based on three public human action …