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• W2752605215 abstract "Human action classification, which is vital for content-based video retrieval and human-machine interaction, finds problem in distinguishing similar actions. Previous works typically detect spatial-temporal interest points (STIPs) from action sequences and then adopt bag-of-visual words (BoVW) model to describe actions as numerical statistics of STIPs. Despite the robustness of BoVW, this model ignores the spatial-temporal layout of STIPs, leading to misclassification among different types of actions with similar numerical statistics of STIPs. Motivated by this, a time-ordered feature is designed to describe the temporal distribution of STIPs, which contains complementary structural information to traditional BoVW model. Moreover, a temporal refinement method is used to eliminate intra-variations among time-ordered features caused by performers' habits. Then a time-ordered BoVW model is built to represent actions, which encodes both numerical statistics and temporal distribution of STIPs. Extensive experiments on three challenging datasets, i.e., KTH, Rochster and UT-Interaction, validate the effectiveness of our method in distinguishing similar actions." @default.
• W2752605215 created "2017-09-15" @default.
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• W2752605215 date "2017-07-01" @default.
• W2752605215 modified "2023-09-27" @default.
• W2752605215 title "Time-ordered spatial-temporal interest points for human action classification" @default.
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• W2752605215 doi "https://doi.org/10.1109/icme.2017.8019477" @default.
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