Content Based Copy Detection with Coarse Audio-Visual Fingerprints

Date

2009-01-01

Author

Saracoglu, Ahmet
Esen, Ersin
Ates, Tugrul K.
Acar, Banu Oskay
Zubari, Uenal
Ozan, Ezgi C.
Ozalp, Egemen
Alatan, Abdullah Aydın
Ciloglu, Tolga

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

192
views

0
downloads

Content based copy detection (CBCD) emerges as a viable choice against active detection methodology of watermarking. The very first reason is that the media already under circulation cannot be marked and secondly, CBCD inherently can endure various severe attacks, which watermarking cannot. Although in general, media content is handled independently as visual and audio in this work both information sources are utilized in a unified framework, in which coarse representation of fundamental features are employed. From the copy detection perspective, number of attacks on audio content is limited with respect to visual case. Therefore audio, if present, is an indispensable part of a robust video copy detection system. In this study, the validity of this statement is presented through various experiments on a large data set.

Subject Keywords

Content-based copy detection , Coarse fingerprints , Duplicate retrieval

URI

https://hdl.handle.net/11511/41387

DOI

https://doi.org/10.1109/cbmi.2009.12

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Content Based Video Copy Detection with Coarse Features Esen, Ersin; Saracoglu, Ahmet; Ates, Tugrul K.; Acar, Banu Oskay; Zubari, Uenal; Alatan, Abdullah Aydın (2009-01-01) Content Based Copy Detection is an alternative approach to invisible watermarking for tracking duplicate data. Primary stages are creating a database using the features belonging to the original data and searching query data in terms of its features in this database. Features must be robust against targeted attacks and discriminative enough to distinguish different content. In this work, we propose reducing the precision of feature values to attain robustness and increasing the number and dimension of featu...
CONTENT BASED HYPERSPECTRAL IMAGE RETRIEVAL USING BAG OF ENDMEMBERS IMAGE DESCRIPTORS Omruuzun, Fatih; Demir, Begum; Bruzzone, Lorenzo; Çetin, Yasemin (2016-08-24) This paper proposes a novel system for fast and accurate content based retrieval of hyperspectral images. The proposed system aims at retrieving hyperspectral images that have both similar spectral characteristics associated with specific materials and fractional abundances to the query image. It consists of two modules. The first module characterizes the query and the target hyperspectral images in the archive by two descriptors: 1) a binary spectral descriptor representing spectral characteristics of dist...
Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery Ergul, Mustafa; Sen, Nigar; Okman, O. Erman (2014-05-07) Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data. Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades. Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to characterize the background. The global SVDD needs a tra...
Image Annotation With Semi-Supervised Clustering Sayar, Ahmet; Yarman Vural, Fatoş Tunay (2009-09-16) Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using three types of side information. The first one is the topic probability information obtained from the text document associated with the image. The second is the orientation an...
Distributed Content Based Video Identification in Peer-to-Peer Networks: Requirements and Solutions Koz, Alper; Lagendijk, R. (Inald) L. (2017-03-01) 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 n...

Citation Formats

A. Saracoglu et al., “Content Based Copy Detection with Coarse Audio-Visual Fingerprints,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41387.