&lt;title&gt;Special-effect edit detection using VideoTrails: a comparison with existing techniques&lt;/title&gt;

Kobla, Vikrant; DeMenthon, Daniel; Doermann, David

doi:10.1117/12.333850

Cited by 20 publications

(16 citation statements)

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“…We used the value of l = 2.8; we also compared D and D k i+k/2+1 , respectively. This modification was also suggested by [10]. Variance curve [13].…”

Section: Methodsmentioning

confidence: 71%

“…A number of researchers presented their evaluation of different approaches [3,6,10]. The recall and the precision ratio were often computed, the former evaluating the number of missed detections and the latter the number of false alarms:…”

Section: Related Workmentioning

confidence: 99%

“…Kobla et al [10] did a comparatively exhaustive comparison on special effect detection between their algorithm VideoTrails and four other algorithms. These four algorithms were: (1) the plateau detection algorithm of Yeo and Liu [20]; (2) the variance curve approach of Meng et al [13]; (3) the twin comparison approach of Zhang et al [21]; and (4) the chromatic edit model approach of Song et al [18].…”

Section: Related Workmentioning

confidence: 99%

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Scene-Based Shot Change Detection and Comparative Evaluation

Cheong

2000

Computer Vision and Image Understanding

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A key step for managing a large video database is to partition the video sequences into shots. Past approaches to this problem tend to confuse gradual shot changes with changes caused by smooth camera motions. This is in part due to the fact that camera motion has not been dealt with in a more fundamental way. We propose an approach that is based on a physical constraint used in optical flow analysis, namely, the total brightness of a scene point across two frames should remain constant if the change across two frames is a result of smooth camera motion. Since the brightness constraint would be violated across a shot change, the detection can be based on detecting the violation of this constraint. It is robust because it uses only the qualitative aspect of the brightness constraint-detecting a scene change rather than estimating the scene itself. Moreover, by tapping on the significant know-how in using this constraint, the algorithm's robustness is further enhanced. Experimental results are presented to demonstrate the performance of various algorithms. It is shown that our algorithm is less likely to interpret gradual camera motions as shot changes, resulting in a better precision performance than most other algorithms. However, its performance deteriorates under large camera or object motions. A twin-threshold scheme is proposed to improve its robustness.

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“…We used the value of l = 2.8; we also compared D and D k i+k/2+1 , respectively. This modification was also suggested by [10]. Variance curve [13].…”

Section: Methodsmentioning

confidence: 71%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Scene-Based Shot Change Detection and Comparative Evaluation

Cheong

2000

Computer Vision and Image Understanding

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“…The performance and complexity of this method are dependent on the clustering and geometrical analysis algorithms used in a multidimensional space. In [20], a comparison between the authors' VideoTrails (VT) approach and various existing algorithms for scene change detection is presented.…”

Section: Related Workmentioning

confidence: 99%

“…The Generalized Likelihood Ratio algorithm is the relevant method for solving this hypothesis testing problem. Thus, for the sequential case, the GLR algorithm can be written as (18) The log-likelihood ratio for the sequence of vectors , is (19) In the context of the local asymptotic approach, similarly to (14), the second-order Taylor expansion of the log-likelihood ratio around , for the sample is [24]: (20) where is the Fisher information matrix, and is the efficient score for sample given by…”

Section: B Non-additive Modelingmentioning

confidence: 99%

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

Lelescu¹,

Schonfeld

2003

IEEE Trans. Multimedia

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Abstract-The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. In this paper, a novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.

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Low Level Analysis of Video Using Spatiotemporal Pixel Blocks

Naci

Hanjalic

2006

Multimedia Content Representation, Classification and Security

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<title>Special-effect edit detection using VideoTrails: a comparison with existing techniques</title>

Cited by 20 publications

References 0 publications

Scene-Based Shot Change Detection and Comparative Evaluation

Scene-Based Shot Change Detection and Comparative Evaluation

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

Low Level Analysis of Video Using Spatiotemporal Pixel Blocks

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