Improved Affinity Propagation for Gesture Recognition

This paper presents a new clustering method based on the Affinity Propagation (AP) for gesture recognition. AP has been successfully applied to broad areas of computer science research because of its better clustering performance over traditional methods such as k-means. In order to obtain high quality sets of clusters, the original Affinity Propagation algorithm exchanges real-valued messages between all pairs of data points iteratively until convergence. Therefore, the original AP is not suitable for handling big data. In this paper, we add two improvements to the original AP. In order to increase the processing speed, we define the continuous optical flow in video sequence and use it to reduce the number of the data to be clustered. In order to guarantee the accuracy, we define a function for evaluating the preference according to the data distribution