A compact, effective descriptor for video copy detection

Yeh, Mei-Chen; Cheng, Kwang-Ting

doi:10.1145/1631272.1631375

Cited by 29 publications

(16 citation statements)

References 13 publications

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“…Luminance based descriptors [9], dominant colour [10], gradient based features [11] and texture feature [12] are also attempted. A graph based technique has been explained in [13] to define a spatial correlation descriptor where edges represent the content proximity of the regions in the frame and a node in the graph denotes a region in the frame.…”

Section: Fingerprintmentioning

confidence: 99%

A Survey on Video Content Identification Tool

Patil¹,

Satao²

2014

IJCA

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Now-a-days, the search engines available are text-based search engines. Thus using text-based search engines one can efficiently search for the desired video. Many times it happens like video name that has fired as a query, contains irrelevant data. Even recently it is found that some illegal information is communicated via video by embedding it into a longer video. And also it is found that broadcast channels and IPTV services many times use same digital videos. An efficient method of consuming, storing and retrieving such vast amounts of videos is essential. This has led to the emergence of video copy detection as an active area of research. In this survey, a study of different MPEG standard, challenges in video copy detection, brief idea about video fingerprint and its application are discussed.

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Section: Fingerprintmentioning

confidence: 99%

A Survey on Video Content Identification Tool

Patil¹,

Satao²

2014

IJCA

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“…In turn, such features are usually keypoint-based [11], [12], [13], [14], block-based [2], [15], [16] or global [17], [18]. For keypointbased approaches, SIFT features [14], [19], [20] or local descriptors around Harris interest points [12], [13], [21], [22] are a common design choice.…”

Section: Related Workmentioning

confidence: 99%

“…Such approaches can attain increased robustness to content modifications, but also entail increased computational costs. Block-based approaches [2], [15], [16] involve the calculation of certain properties of pre-defined spatial regions in the frame. Block-based methods typically entail reduced computational costs, but are also less robust to transformations such as scaling and rotation.…”

Section: Related Workmentioning

confidence: 99%

The MPEG-7 Video Signature Tools for Content Identification

Paschalakis¹,

Iwamoto

Brasnett

et al. 2012

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-This paper presents the core technologies of the Video Signature Tools recently standardized by ISO/IEC MPEG as an amendment to the MPEG-7 Standard (ISO/IEC 15938). The Video Signature is a high-performance content fingerprint which is suitable for desktop-scale to web-scale deployment and provides high levels of robustness to common video editing operations and high temporal localization accuracy at extremely low false alarm rates, achieving a detection rate in the order of 96% at a false alarm rate in the order of five false matches per million comparisons. The applications of the Video Signature are numerous and include rights management and monetization, distribution management, usage monitoring, metadata association, and corporate or personal database management. In this paper we review the prior work in the field, explain the standardization process and status, and provide details and evaluation results for the Video Signature Tools.

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“…반면에 다 이나믹 프로그래밍(dynamic programming)을 기반으로 최 적화 문제를 해결함으로써 더욱 정밀한 프레임 단위의 정 합을 가능하게 하는 기법들도 연구되고 있다 [5][6][7] . 하지만 높 은 연산량과 많은 메모리 공간을 요구하는 단점이 있다.…”

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“…하지만 높 은 연산량과 많은 메모리 공간을 요구하는 단점이 있다. [5][6][7] 은 전역 최적화 해결방법이 지만 많은 계산량과 메모리를 요구한다. 본 연구에선 초기 정합을 실시한 후 반복적인 정합 갱신을 통해 연산량을 줄 이면서 효과적으로 정합비용 함수를 최소화한다.…”

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Frame-level Matching for Near Duplicate Videos Using Binary Frame Descriptor

Kim¹,

Lee²,

Jang³

2015

Journal of Broadcast Engineering

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In this paper, we propose a precise frame-level near-duplicate video matching algorithm. First, a binary frame descriptor for near-duplicate video matching is proposed. The binary frame descriptor divides a frame into patches and represent the relations between patches in bits. Seconds, we formulate a cost function for the matching, composed of matching costs and compensatory costs. Then, we roughly determine initial matchings and refine the matchings iteratively to minimize the cost function. Experimental results demonstrate that the proposed algorithm provides efficient performance for frame-level near duplicate video matching.Keyword : Near-duplicate video, frame-level video matching, and binary frame descriptor. . 하지만 동영상 단위 기술자는 동영상의 시간적 관계 를 무시하기 때문에 여러 단편으로 엮인 동영상에 대해 취 약점이 있다. 따라서 동영상의 중복 여부를 보다 정밀하게 판별하기 위해 실제로 중복된 구간을 검출할 필요가 있다. 본 논문에서는 유사중복 동영상 검출에 적합한 이진 프 Ⅰ. 서 론 최근

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A compact, effective descriptor for video copy detection

Cited by 29 publications

References 13 publications

A Survey on Video Content Identification Tool

A Survey on Video Content Identification Tool

The MPEG-7 Video Signature Tools for Content Identification

Frame-level Matching for Near Duplicate Videos Using Binary Frame Descriptor

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