Scene change detection algorithms for content-based video indexing and retrieval

Fernando, W.A.C.; Canagarajah, C N; Bull,

doi:10.1049/ecej:20010302

Cited by 37 publications

(17 citation statements)

References 12 publications

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“…For example, it should be placed at a default position (such as the bottom of the screen) if the speaker disappears after a shot change. We propose a simple shot change detector for video segments solely based on color histogram (see Algorithm 3 5 ) instead of using shot change detectors based on complex models [Sethi and Patel 1995;Faernando et al 2001;Dimou et al 2005]. Our shot change detector proves to be accurate enough even for some very challenging TV series such as "Friends" and "The.Big.Bang.Theory", as shown in Table I.…”

Section: Some Further Considerationsmentioning

confidence: 99%

Speaker-Following Video Subtitles

Kautz

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

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We propose a new method for improving the presentation of subtitles in video (e.g. TV and movies). With conventional subtitles, the viewer has to constantly look away from the main viewing area to read the subtitles at the bottom of the screen, which disrupts the viewing experience and causes unnecessary eyestrain. Our method places on-screen subtitles next to the respective speakers to allow the viewer to follow the visual content while simultaneously reading the subtitles. We use novel identification algorithms to detect the speakers based on audio and visual information. Then the placement of the subtitles is determined using global optimization. A comprehensive usability study indicated that our subtitle placement method outperformed both conventional fixed-position subtitling and another previous dynamic subtitling method in terms of enhancing the overall viewing experience and reducing eyestrain.

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Section: Some Further Considerationsmentioning

confidence: 99%

Speaker-Following Video Subtitles

Kautz

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

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“…Algorithms using motion features (Porter et al, 2000) or developed in the compressed domain (Fernando et al, 2001) propose an interesting alternative solution to pixel and histogram-based methods. Notably, such approaches are fairly robust with respect to camera and object motion.…”

Section: Related Workmentioning

confidence: 99%

Video Segmentation and Structuring for Indexing Applications

Țapu

Zaharia

2011

International Journal of Multimedia Data Engineering and Management

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This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot, the authors construct a static storyboard by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing techniques for similar performances. Finally, the authors propose a shot grouping strategy that iteratively clusters visually similar shots under a set of temporal constraints. Two different types of visual features are exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.

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“…Algorithms using motion features [6] create an alternative to pixel and histograms based methods due to their robustness to camera and object displacement. The techniques developed in compressed domain assure optimal processing time [7], but provide inferior precision and recall rates than histogram based approaches.…”

Section: Shot Boundary Detection Systemmentioning

confidence: 99%

Automatic Multilevel Temporal Video Structuring

Țapu

Zaharia

2011

2011 IEEE Fifth International Conference on Semantic Computing

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Abstract-In this paper we propose a novel and complete video scene segmentation framework, developed on different structural levels of analysis. Firstly, a shot boundary detection algorithm is introduced that extends the graph partition method with a nonlinear scale space filtering technique which increase the detection efficiency with gains of 7,4% to 9,8% in terms of both precision and recall rates. Secondly, static storyboards are formed based on a leap keyframe extraction method that selects a variable number of keyframes, adapted to the visual content variation, for each detected shot. Finally using the extracted keyframes, spatio-temporal coherent shots are clustered into the same scene based on temporal constraints and with the help of a new concept of neutralized shots. Video scenes are obtained with average precision and recall rates of 86%.

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Scene change detection algorithms for content-based video indexing and retrieval

Cited by 37 publications

References 12 publications

Speaker-Following Video Subtitles

Speaker-Following Video Subtitles

Video Segmentation and Structuring for Indexing Applications

Automatic Multilevel Temporal Video Structuring

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