Design and evaluation of an effective and efficient video copy detection system

Natsev, Apostol; Hill, Matthew; Smith, J. R.

doi:10.1109/icme.2010.5583216

Cited by 15 publications

(10 citation statements)

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“…Video resolution was 352×288 and videos were in an AVI format. Video detection time comparisons with previous algorithms are shown in Table 2, from which it can be seen that the detection time was shorter and detection speed was quicker than with the algorithms used in [11][12][13]. …”

Section: Methodsmentioning

confidence: 99%

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A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

2016

J Inf Process Syst

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Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.

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Section: Methodsmentioning

confidence: 99%

“…Video feature points are matched using the SIFT algorithm [11], the CGO algorithm [12], the Harris algorithm [13], and the proposed SURF algorithm. Fig.…”

Section: Video Feature Matchingmentioning

confidence: 99%

A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

2016

J Inf Process Syst

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“…This threshold is tuned on a training set. Detailed comparison of each technique can be found in a related paper [33]. Our solution to the complexity challenge is to use an indexing scheme for fast approximate nearest neighbor (ANN) lookup.…”

Section: Scalable Visual Meme Detectionmentioning

confidence: 99%

“…Our method for detecting visual memes builds upon those for tracking near-duplicates in images and video. Recent foci in near-duplicate detection include speeding up detection on image sequence, frame, or local image points [40], exploring the effect of factors other than visual features [33], and scaling out to web-scale computations using large compute clusters [29]. Compared to copy detection, our work tracks mutual remixes in a large collection rather than matching one query video with reference database.…”

Section: Related Workmentioning

confidence: 99%

Tracking Large-Scale Video Remix in Real-World Events

Xie

Natsev

et al. 2013

IEEE Trans. Multimedia

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Social information networks, such as YouTube, contains traces of both explicit online interaction (such as "like", leaving a comment, or subscribing to video feed), and latent interactions (such as quoting, or remixing parts of a video). We propose visual memes, or frequently re-posted short video segments, for detecting and monitoring such latent video interactions at scale. Visual memes are extracted by scalable detection algorithms that we develop, with high accuracy. We further augment visual memes with text, via a statistical model of latent topics. We model content interactions on YouTube with visual memes, defining several measures of influence and building predictive models for meme popularity. Experiments are carried out on with over 2 million video shots from more than 40,000 videos on two prominent news events in 2009: the election in Iran and the swine flu epidemic. In these two events, a high percentage of videos contain remixed content, and it is apparent that traditional news media and citizen journalists have different roles in disseminating remixed content. We perform two quantitative evaluations for annotating visual memes and predicting their popularity. The joint statistical model of visual memes and words outperform a concurrence model, and the average error is is ±2% for predicting meme volume and ±17% for their lifespan.

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“…X Wu [4] proposed that a video frame is divided into sub-blocks, the maximum position is obtained by calculation the average gray value, position change is used as video feature. Natsev [5] proposed that scale invariant image features and color correlation graph are extracted as video feature, the algorithm is robust and complexity. Boukhari [6] presents a video copy detection system based on testing similarities between textural feature vectors which are extracted from videos, the proposed method is based on weber binarized statistical image features.…”

Section: Introductionmentioning

confidence: 99%

Video Copy Detection based on Uniform Local Binary Pattern

Hou¹,

Wang²,

Liu³

2018

dtcse

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Video copy detection techniques are used to detect copies of video widely, this paper proposed a new algorithm based on spatiotemporal analysis for copy detection and compares detection precision and efficiency with existing algorithms. The descriptor encodes the structure of video key frames by computing uniform Local Binary Pattern with rotation invariance. Besides, Chi-square tests are employed to speed up the matching process. The proposed algorithm can deal with various kinds of video transformations, such as brightness conversion, sharpen, contrast and gray scale, especially for video rotation which is not well addressed in existing algorithms. The results of experiments tested on TRECVID 2015 dataset, experimental results indicate that precision and recall are improved, proposed algorithm is with good robustness and discrimination accuracy, detection performance is improved further.

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Design and evaluation of an effective and efficient video copy detection system

Cited by 15 publications

References 10 publications

A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor

Tracking Large-Scale Video Remix in Real-World Events

Video Copy Detection based on Uniform Local Binary Pattern

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