Near-duplicate Video Detection Algorithm Based on Global GSP Feature and Local ScSIFT Feature Fusion
Xidao Luan, Yuxiang Xie, Jingmeng He, Lili Zhang, Chen Li, Xin Zhang · Journal of Physics Conference Series · 2018
The main problem with near-duplicate video detection is the high computational complexity and the low efficiency. Near-duplicate video detection methods based on global feature is running fast but with low accuracy, on the contrary, methods based on local feature is accurate, but the calculation is large and time-consuming. Therefore, a near-duplicate video detection algorithm combining global GSP feature and local ScSIFT feature is proposed. Firstly, the video clips and the query ones are discretized into a set of key frame sequences, and the temporal information is recorded at the same time. Secondly, by filtering the video clips on global Gaussian-Scale pyramid feature, similar video clips are selected as the candidates to assure the high recall. Then, combined with the temporal features of the keyframes, the candidate videos are further detected by the local ScSIFT feature to obtain a higher precision. Experimental results show that the proposed algorithm can improve the accuracy of near-duplicate video detection on the basis of guaranteeing the timeliness of the algorithm.