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With the explosive growth of web videos in recent years, large-scaleContent-Based Video Retrieval (CBVR) becomes increasingly essential in videofiltering, recommendation, and copyright protection. Segment-level CBVR(S-CBVR) locates the start and end time of similar segments in finergranularity, which is beneficial for user browsing efficiency and infringementdetection especially in long video scenarios. The challenge of S-CBVR task ishow to achieve high temporal alignment accuracy with efficient computation andlow storage consumption. In this paper, we propose a Segment Similarity andAlignment Network (SSAN) in dealing with the challenge which is firstly trainedend-to-end in S-CBVR. SSAN is based on two newly proposed modules in videoretrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module toreduce redundant frame features, (2) A robust Similarity Pattern Detection(SPD) module for temporal alignment. In comparison with uniform frameextraction, SKE not only saves feature storage and search time, but alsointroduces comparable accuracy and limited extra computation time. In terms oftemporal alignment, SPD localizes similar segments with higher accuracy andefficiency than existing deep learning methods. Furthermore, we jointly trainSSAN with SKE and SPD and achieve an end-to-end improvement. Meanwhile, the twokey modules SKE and SPD can also be effectively inserted into other videoretrieval pipelines and gain considerable performance improvements.Experimental results on public datasets show that SSAN can obtain higheralignment accuracy while saving storage and online query computational costcompared to existing methods.

Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval