WITHDRAWN: Video structure syntax mining based on Hidden Markov Model in sports video

Abstract

Abstract On the basis of shot segmentation, an improved Rate Sensitive Competitive Learning algorithm (RSCLA) is proposed for unsupervised shot clustering based on NBA basketball video, which converts video stream data into symbol sequence. Aiming at the characteristics of sequence correlation, time correlation and no clear transaction concept in Video Association rules, this paper improved the traditional Apriori algorithm and proposed a video Association Rules Mining Algorithm based on time-based window computing support. The periodic or semi-periodic structural grammar patterns in video are explored with frequent sets of association rules to guide the establishment of video structure semantic mining model. Aiming at the limitation of string matching, this paper proposes a HMM-based pattern mining method to analyze high-level video events, identify and locate free throw events in basketball videos. The experimental results show that the four-state HMM is the inner free throw mode of basketball video and achieves good performance.