  With the explosive growth of web videos in recent years, large-scale
Content-Based Video Retrieval (CBVR) becomes increasingly essential in video
filtering, recommendation, and copyright protection. Segment-level CBVR
(S-CBVR) locates the start and end time of similar segments in finer
granularity, which is beneficial for user browsing efficiency and infringement
detection especially in long video scenarios. The challenge of S-CBVR task is
how to achieve high temporal alignment accuracy with efficient computation and
low storage consumption. In this paper, we propose a Segment Similarity and
Alignment Network (SSAN) in dealing with the challenge which is firstly trained
end-to-end in S-CBVR. SSAN is based on two newly proposed modules in video
retrieval: (1) An efficient Self-supervised Keyframe Extraction (SKE) module to
reduce redundant frame features, (2) A robust Similarity Pattern Detection
(SPD) module for temporal alignment. In comparison with uniform frame
extraction, SKE not only saves feature storage and search time, but also
introduces comparable accuracy and limited extra computation time. In terms of
temporal alignment, SPD localizes similar segments with higher accuracy and
efficiency than existing deep learning methods. Furthermore, we jointly train
SSAN with SKE and SPD and achieve an end-to-end improvement. Meanwhile, the two
key modules SKE and SPD can also be effectively inserted into other video
retrieval pipelines and gain considerable performance improvements.
Experimental results on public datasets show that SSAN can obtain higher
alignment accuracy while saving storage and online query computational cost
compared to existing methods.
