Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 67-76.Huge and increasing amount of videos broadcast through networks has raised
the need of automatic video copy detection for copyright protection. Recent
developments in multimedia technology introduced content-based copy detection
(CBCD) as a new research field alternative to the watermarking approach for
identification of video sequences.
This thesis presents a multimodal framework for matching video sequences
using a three-step approach: First, a high-level face detector identifies facial
frames/shots in a video clip. Matching faces with extended body regions gives
the flexibility to discriminate the same person (e.g., an anchor man or a political
leader) in different events or scenes. In the second step, a spatiotemporal sequence
matching technique is employed to match video clips/segments that are similar
in terms of activity. Finally the non-facial shots are matched using low-level
visual features. In addition, we utilize fuzzy logic approach for extracting color
histogram to detect shot boundaries of heavily manipulated video clips. Methods
for detecting noise, frame-droppings, picture-in-picture transformation windows,
and extracting mask for still regions are also proposed and evaluated.
The proposed method was tested on the query and reference dataset of CBCD
task of TRECVID 2008. Our results were compared with the results of top-8 most
successful techniques submitted to this task. Experimental results show that the
proposed method performs better than most of the state-of-the-art techniques,
in terms of both effectiveness and efficiency.Küçüktunç, OnurM.S