A novel feature representation method based on super-interest points is proposed and applied to human action recognition. Interest point features describe the salient change of local point information when humans are in the state of movement

however

the drawback of these features is that the rich spatio-temporal relationships among the discrete interest points are not being used. According to the spatio-temporal distance of interest points

a broad first search neighbors algorithm is used to cluster these adjacent points into super-interest point. The super-interest point features reflect the variety of the human limb in a certainly spatio-temporal scope. Compared to the existing interest point method

the spatial and temporal information among the points

are all included in our super-interest points

making the features more discriminative. Finally

a two hierarchical classifier recognizes the human actions using super-interest point features. The experimental results show that our method achieves a good recognition rate.