Video copy detection

Law-To, Julien; Chen, Li; Joly, Alexis; Laptev, Ivan; Buisson, Olivier; Gouet-Brunet, Valérie; Boujemaa, Nozha; Stentiford, Fred

doi:10.1145/1282280.1282336

Cited by 224 publications

(9 citation statements)

References 15 publications

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“…Thus, it is preferable to use searching methods that are looking for a close match of the query instead of an exact match. This problem is a nearest neighbor problem in the binary space [29], which can be simply handled by an exhaustive search but the computational requirement is extremely high. For a practical CBCD system, the size of the video database is huge with tens of millions of videos; fast approximate search algorithms are commonly used.…”

Section: Tiri-sbvf Based Content-based Copy Detection Systemmentioning

confidence: 99%

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Po¹,

Li²,

Xu³

et al. 2016

JSIP

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Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the "natural parts" (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion); geometry attacks (letter box and rotation); and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.

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Section: Tiri-sbvf Based Content-based Copy Detection Systemmentioning

confidence: 99%

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Po¹,

Li²,

Xu³

et al. 2016

JSIP

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“…Table 5 shows that MMMV and MPMV algorithms consume less space for signatures than the other algorithms except the method called "Temporal" [9].…”

Section: Number Of Feature Parameters Per Framementioning

confidence: 99%

“…Existing methods of video CBCD usually extract signatures, key frames, or fingerprints from the images of video stream and compare them with the database that contains the features of original videos [2,6,[13][14][15][16]. Several spatial or temporal features of videos are considered as signatures of videos such as intensity of pixels, color histograms, and motion [3,9]. The main advantage of CBCD over watermarking is that signature extraction can be done even if the video is distributed over the Internet or other media because the unique signature is part of the video itself.…”

Section: Introductionmentioning

confidence: 99%

Content-based video copy detection based on motion vectors estimated using a lower frame rate

Taşdemir

Çetin

2014

SIViP

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We propose a motion vector-based video content-based copy detection method. One of the signatures of a given video is motion vectors extracted from image sequences. However, when consecutive image frames are used, the resulting motion vectors are not descriptive enough because most vectors are either too small or they appear to scatter in all directions. We calculate motion vectors in a lower frame rate than the actual frame rate of the video to overcome this problem. As a result, we obtain large vectors and they represent a given video in a robust manner. We carry out experiments for various parameters and present the results. © 2014 Springer-Verlag London

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“…Yapılan deneylerde görsel ipuçları kullanılarak elde edilen yerel tanımlayıcıların daha fazla hesaplama gücü gerektirmesine ragmen global tanımlayıcılara göre daha gürbüz çalıştıkları gözlenmiştir [3]. Video içerisindeki görsel ipuçları renk, doku veya hareket gibi farklı kaynaklardan üretilebilir.…”

Section: Introductionunclassified

Video copy detection using motion co-occurrence feature

Arabaci

Esen

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Özetçe -İçerik tabanlı video kopya bulma, özellikle telif hakkına sahip ve ticari amaçla üretilen videoların dagıtımında ve takip edilmesinde önemli bir rol oynamaktadır. Problemin çözümü için genellikle video çerçevesi içerisindeki ilgi noktalarını kullanan yerel tanımlayıcılar veya tüm çerçeve içerigini kullanan global tanımlayıcılar kullanılmaktadır. Yerel tanımlayıcılar kullanılarak elde edilen sonuçlar başarılı olmakla birlikte öznitelik çıkarma ve indeksleme işlemleri çok zaman almaktadır. Bu çalışmada, içerik tabanlı video kopya bulma problemi için hareket temelli özniteliklerden biri olan hareket birliktelik özniteligi (HBÖ) önerilmiştir. HBÖ, global bir öznitelik olmasına karşın uzamsal ve zamansal komşulugu kullanmasından dolayı içerisinde yerel bilgiyi barındırmaktadır. Aynı zamanda, video akışı içerisinde yer alan hareket bilgisinin dogrudan kullanılması ile çıkarılmaktadır. Bu sayede, öznitelik çıkarma işlemi hızlı biŗ sekilde yapılabilmektedir. Önerilen yöntemin, özellikle hareket bilgisinin korundugu ataklara karşı etkili oldugu gözlenmiştir.Anahtar Kelimeler-içerik tabanlı video kopya bulma, hareket birliktelik özniteligi.Abstract-Content-based video copy detection (CBCD) has an important role especially in distributing and tracking of copyright and commercial videos. Basically, two main descriptors are used for CBCD one of which, using the interest points in keyframes, the other using the content of a whole keyframe. Even if the local descriptor-based approaches have good results in CBCD problem, it takes too much time for extracting and indexing features. In this study, one of the motion-based features named as motion co-occurrence feature (MCF) is proposed for the solution of CBCD problem. Basically, MCF is a global feature that uses local information by considering spatial and temporal neighbourhood of motion vectors. Additionally, MCF can be extracted directly from the motion information of the video bitstream. Thus, feature extraction process can be performed quickly. The results show that the proposed method is effective especially for the attacks in which the motion information is preserved.

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Video copy detection

Cited by 224 publications

References 15 publications

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Shearlet Based Video Fingerprint for Content-Based Copy Detection

Content-based video copy detection based on motion vectors estimated using a lower frame rate

Video copy detection using motion co-occurrence feature

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