A POI classification semantic annotation algorithm based on HMM

Abstract

Given the dense distribution of points of interest in cities, it is difficult to semantically label trajectories by distance. This paper proposes a classification semantic labeling algorithm based on hidden Markov model (HMM). An algorithm based on time and space constraints is used to identify the stay points in the trajectory, and a density-based clustering algorithm is used to identify the user’s individual significant places. On the basis of the identification of individual points of interest according to the stay time and stay period, a method based on HMM is used to label public points of interest for the dense areas of points of interest. When calculating the observation probability matrix, a dynamic time function is used to simulate the influence radius of public points of interest in different periods. The simulation result shows that compared with the traditional method, the model has a significant improvement in the accuracy of labeling individual points of interest. At the same time, in areas with dense points of interest, the algorithm improves the accuracy of labeling by 6% compared with the traditional HMM.