You are here

Content identification using video tomography

Download pdf | Full Screen View

Date Issued:
2008
Summary:
Video identification or copy detection is a challenging problem and is becoming increasingly important with the popularity of online video services. The problem addressed in this thesis is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed and implemented. The nature of the signature makes it independent to the most commonly video transformations. The signatures are generated for video shots and not individual frames, resulting in a compact signature of 64 bytes per video shot. The signatures are matched using simple Euclidean distance metric. The results show that videos can be identified with 100% recall and over 93% precision. The experiments included several transformations on videos.
Title: Content identification using video tomography.
324 views
258 downloads
Name(s): Leon, Gustavo A.
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2008
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: vi, 61 p. : ill. (some col.)
Language(s): English
Summary: Video identification or copy detection is a challenging problem and is becoming increasingly important with the popularity of online video services. The problem addressed in this thesis is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed and implemented. The nature of the signature makes it independent to the most commonly video transformations. The signatures are generated for video shots and not individual frames, resulting in a compact signature of 64 bytes per video shot. The signatures are matched using simple Euclidean distance metric. The results show that videos can be identified with 100% recall and over 93% precision. The experiments included several transformations on videos.
Identifier: 665167851 (oclc), 2783207 (digitool), FADT2783207 (IID), fau:3542 (fedora)
Note(s): by Gustavo A. Leon.
Thesis (M.S.C.S.)--Florida Atlantic University, 2008.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.
Subject(s): Biometric identification
High performance computing
Image processing -- Digital techniques
Multimedia systems -- Security measures
Persistent Link to This Record: http://purl.flvc.org/FAU/2783207
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU