Probabilistic approach to K-nearest neighbor video retrieval
In this paper we propose a probabilistic approach to retrieve video clips similar to a given query video clip. In our approach the video clips are partitioned into video segments based on their content homogeneity, and video segments in the database are connected to construct candidate clips and compared with the query clip for their similarity (or distance) during the query process. An efficient scheme is developed to estimate the probability density functions of the distances between the candidate clips and query clip, and based on these density functions, two methods are devised to reduce the number of candidate clips for comparison to speed up the retrieval process. Experimental results show that our proposed approach can notably speed up the retrieval of similar video clips, while maintaining high retrieval accuracy.