2 research outputs found
Robust Content Identification and De-Duplication with Scalable Fisher Vector In video with Temporal Sampling
Title from PDF of title page, viewed august 29, 2017Thesis advisor: Zhu LiVitaIncludes bibliographical references (pages 41-43)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017Robust content identification and de-duplication of video content in networks and caches have
many important applications in content delivery networks. In this work, we propose a scalable
hashing scheme based Fisher Vector aggregation of selected key point features, and a frame
significance function based non-uniform temporal sampling scheme on the video segments,
to create a very compact binary representation of the content fragments that is agnostic to the
typical coding and transcoding variations. The key innovations are a key point repeatability
model that selects the best key point features, and a non-uniform sampling scheme that
significantly reduces the bits required to represent a segment, and scalability from PCA feature
dimension reduction and Fisher Vector features, and Simulation with various frame size and
bit rate video contents for DASH streaming are tested and the proposed solution have very
good performance of precision-recall, achieving 100% precision in duplication detection with
recalls at 98% and above range.Introduction -- Software description -- Image processing -- SIFT feature extraction -- Principal component analysis -- Fisher vector aggregation -- Simulation results and discussions -- Conclusion and future work -- Appendi