{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/61367"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/61367","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Robust Content Identification and De-Duplication with Scalable Fisher Vector In video with Temporal Sampling","abstract":"Robust 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.","abstract_html":"Robust 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.","abstract_has_math":false,"creators":["Gadiparthi, Lakshmi Prasanna"],"institution":"University of Missouri--Kansas City","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Electrical Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Li, Zhu"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-24T05:18:08Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/61367","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Li, Zhu"]},{"key":"dc:creator","label":"Author","values":["Gadiparthi, Lakshmi Prasanna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-08-22T16:35:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-08-22T16:35:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Kansas City"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/61367"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page, viewed august 29, 2017","Thesis advisor: Zhu Li","Vita","Includes bibliographical references (pages 41-43)","Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017"]},{"key":"dc:description.abstract","label":"Abstract","values":["Robust 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."]},{"key":"dc:title","label":"Title","values":["Robust Content Identification and De-Duplication with Scalable Fisher Vector In video with Temporal Sampling"]}]}],"canonical_facts":{"dc:contributor.advisor":["Li, Zhu"],"dc:creator":["Gadiparthi, Lakshmi Prasanna"],"dc:date.accessioned":["2017-08-22T16:35:37Z"],"dc:date.available":["2017-08-22T16:35:37Z"],"dc:date.issued":["2017"],"dc:description":["Title from PDF of title page, viewed august 29, 2017","Thesis advisor: Zhu Li","Vita","Includes bibliographical references (pages 41-43)","Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017"],"dc:description.abstract":["Robust 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."],"dc:identifier.uri":["https://hdl.handle.net/10355/61367"],"dc:language.iso":["en_US"],"dc:publisher":["University of Missouri--Kansas City"],"dc:title":["Robust Content Identification and De-Duplication with Scalable Fisher Vector In video with Temporal Sampling"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering (UMKC)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:18:08Z"}