Image Signature Robust to Caption Superimposition for Video Sequence Identification

Iwamoto, Kota; Kasutani, E.; Yamada, Akio

doi:10.1109/icip.2006.313046

Cited by 22 publications

(7 citation statements)

References 10 publications

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“…Type a) can be detected by almost all approaches. Some approaches focus on detecting a) and pay more attention to computational efficiency [4,7,21,22]. Interest point approaches are typically used to detect types b) and c) [9,10,11,13].…”

Section: Related Workmentioning

confidence: 99%

Scene duplicate detection based on the pattern of discontinuities in feature point trajectories

Takimoto

Satoh

et al. 2008

Proceedings of the 16th ACM International Conference on Multimedia

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The paper is aiming to detect and retrieve videos of the same scene (scene duplicates) from broadcast video archives. Scene duplicate is composed of different pieces of footage of the same scene, the same event, at the same time, but from the different viewpoints. Scene duplicate detection would be particularly useful to identify the same event reported in different programs from different broadcast stations. The approach should be invariant to viewpoint changes. We focused on object motion in videos and devised a video matching approach based on the temporal pattern of discontinuities obtained from feature point trajectories. We developed an acceleration method based on the discontinuity pattern, which is more robust to variations in camerawork and editing than conventional features, to dramatically reduce the computation burden. We compared our approach with an existing video matching method based on the local feature of keyframe. The spatial registration strategy of this method was also used with the proposed approach to cope with visually different unrelated video pairs. The performance and effectiveness of our approach was demonstrated on actual broadcasted videos.

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Section: Related Workmentioning

confidence: 99%

Scene duplicate detection based on the pattern of discontinuities in feature point trajectories

Takimoto

Satoh

et al. 2008

Proceedings of the 16th ACM International Conference on Multimedia

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“…Lee performed gradient calculation on the video block to extract video feature [3]. Iwamoto performed estimation of luminance edge on the video blocks in eight directions to extract video feature [4].…”

Section: Calculation and Matching Of Video Hashesmentioning

confidence: 99%

Real-time video copy detection based on Hadoop

Lian

et al. 2016

2016 Sixth International Conference on Information Science and Technology (ICIST)

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With the development of multimedia technology and Internet, the amount of videos in the Internet is increasing quickly. Among the large amount of videos in the Internet, a considerable number of them are copies of original videos, which are simply revised versions of the original ones. The purpose of video copy detection technology is to detect copy videos, which has important applications in video tracking, video content retrieval, video copyright protection and other aspects. The current problem is that real-time video copy detection is often difficult to achieve due to the large amount of video data. Hadoop is a distributed computing platform which is designed for deployment in inexpensive hardware and suitable for those applications with a large data set. All of these characteristics could just meet the requirements of real-time video copy detection technology. In this paper, an attempt is done to develop a real-time video copy detection system based on Hadoop platform, and two video copy detection algorithms are implemented on Hadoop platform, which are the method based on brightness sequence and the method based on TIRI-DCT respectively, and their performances are compared. Experiments show that the use of Hadoop platform can significantly improve the efficiency of video copy detection, which has important practical significance for video tracking and real-time video content retrieval application.

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“…And signatures of each frame are computed independent of other frames. Spatial video signatures include luminance pattern [1], differential luminance pattern [2], gradient pattern [3], and edge pattern [4].…”

Section: A Video Signature Extractingmentioning

confidence: 99%