Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Distributed Content Based Video Identification in Peer-to-Peer Networks: Requirements and Solutions
Date
2017-03-01
Author
Koz, Alper
Lagendijk, R. (Inald) L.
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
14
views
0
downloads
In this paper, we first discuss the essential requirements for a fingerprint (perceptual hash)-based distributed video identification system in peer-to-peer (P2P) networks in comparison with traditional central database implementations of fingerprints. This discussion reveals that first, fingerprint sizes of existing video fingerprint methods are not compatible with the cache sizes of current P2P clients; second, fingerprint extraction durations during a query are not at tolerable levels for a user in the network; third, the repetitive patterns in the extracted fingerprints avoid the uniform distribution of storage and traffic load among the peers; and finally, the existing methods do not provide a solution to synchronize the fingerprint extraction from the shared video and queried video. In order to solve the mentioned requirements, we propose a baseline method using only the difference of video framemeans, which decreases the fingerprint sizes to typical cache sizes, by increasing the granularity levels from seconds to minutes. We then develop a novel algorithm which utilizes reference points on one-dimensional frame mean sequence for the synchronization of fingerprint extraction. This algorithm is extended with a hierarchical decoding approach based on Gaussian scales, which only decodes a subset of video frames without needing a full decoding. Finally, an analysis on the effect of design parameters to the fingerprint probability distribution is performed to avoid repetitive patterns. Our ultimate solution reduces the fingerprint sizes into kilobytes, extraction time to seconds, and search duration into milliseconds, and achieves about 90% detection rates with 1-4 min granularities, while enabling a fair distribution of storage load among the peers at the same time.
Subject Keywords
Video identification
,
Video copy detection
,
Perceptual hash
,
Peer-to-peer (P2P)
,
Gaussian scales
,
Fingerprint
,
Distributed hash table
,
Content search
,
Access right management
URI
https://hdl.handle.net/11511/62596
Journal
IEEE TRANSACTIONS ON MULTIMEDIA
DOI
https://doi.org/10.1109/tmm.2016.2625198
Collections
Center for Image Analysis (OGAM), Article