A new video similarity measure model based on video time density function and dynamic programming

In this paper, we propose a novel video similarity measure model using video time density function (VTDF) and dynamic programming. First, we employ VTDF to describe the density of video activities in time domain by calculating the inter-frame mutual information. Second, a temporal partition solution is applied to divide each video sequence into equi-sized temporal segments. Third, a new VTDF based similarity measure using correlation is calculated to measure the similarity between two temporal segments. Fourth, dynamic programming is then developed to find the optimal non-linear mapping between two video sequences. A new normalized similarity measure function combing both visual characteristics and temporal information together is to evaluate the semantic similarity of two video sequences. Experimental results show that the proposed measurement model is effective to explore the semantic similarity of video sequences.