Metrics for shot boundary detection in digital video sequences

Ford, Ralph M.; Robson, Craig; Temple, Daniel; Gerlach, Michael

doi:10.1007/s005300050003

Cited by 55 publications

(17 citation statements)

References 18 publications

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“…Gargi et al [5] have evaluated and characterized the performance of number of shot detection methods using color histogram, moving picture expert group compression parameter information and image block motion matching. Ford et al [6] have reported results on various histogram test statistics, pixel difference. Yuan et al [7] have presented a comprehensive review of existing approaches and identified major challenges to shot boundary detection, according to them elimination of disturbances due to motion of large object and camera is a challenge in shot boundary detection.…”

Section: Introductionmentioning

confidence: 99%

Shot Boundary Detection for a Video under the Influence of Illumination: A Adaptive Thresholding Approach

Bhalotra¹,

Ghuge²,

Shinde³

2012

IJCA

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Video Shot boundary detection is the process of automatically detecting the boundaries between shots in video. Shot Boundary detection is an important fundamental process in video data access, indexing, search and retrieval. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques. Detection of gradual transition and elimination of disturbances caused by illumination change is a major challenge in shot boundary detection technique. These disturbances are often mistaken as shot boundaries. It is a crucial task to develop a method that is not only insensitive to illumination change but also sensitive to detect shot change. An algorithm is proposed for shot boundary detection in presence of illumination change. This is very important for accurate detection of shot boundaries and very useful in content based analysis of video. First the algorithm removes illumination change using discrete cosine transform and discrete wavelet transform. Then shot boundaries are detected using normal difference & wavelet difference. A shot boundary is detected when the feature difference shows sharp change greater than threshold. Experimental study is performed on number of videos that include significant illumination change. The performance of proposed algorithm is better as compared to existing techniques General TermsVideo shot boundary detection, Image processing, Pattern recognition.

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Section: Introductionmentioning

confidence: 99%

Shot Boundary Detection for a Video under the Influence of Illumination: A Adaptive Thresholding Approach

Bhalotra¹,

Ghuge²,

Shinde³

2012

IJCA

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“…While the first frame difference indicator follows the same principle adopted by all existing work [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] that, if there exist a cut at the current frame, there should exists some content difference between the two neighbouring frames. The second frame difference indicator is mainly used to verify such a possible cut by examining the difference between the current frame and a frame some distance away [14,18] to overcome the false positives caused by factors other than cuts, such as motion, camera movement, or editing effect etc.…”

Section: Constructing Content-aware Indicatorsmentioning

confidence: 99%

Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

Chen

Ren

Jiang

2010

Multimed Tools Appl

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Abstract. In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result,indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic.

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“…A cross transition model ST-JPI can be predicted by the last JPI, with Eqs. (10) and (11). A dither transition model ST-JPI can be predicted by the last JPI, using Eq.…”

Section: Spatial-temporal Pattern Of Gradual Transitionsmentioning

confidence: 99%

“…An integrated method was presented by Ford et al [11,12] for detecting and classifying transitions in uncompressed video sequences. Multiple estimation strategies for frame distances are used: color histogram distance, statistics-based distance, and pixel di erence distance.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal joint probability images for video segmentation

Wei

2000

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101)

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E ective annotation and content-based search for videos in a digital library require a preprocessing step of detecting, locating and classifying scene transitions, i.e., temporal video segmentation. This paper proposes a novel approachspatial-temporal joint probability image (ST-JPI) analysis for temporal video segmentation. A joint probability image (JPI) is derived from the joint probabilities of intensity values of corresponding points in two images. The ST-JPT, which is a series of JPIs derived from consecutive video frames, presents the evolution of the intensity joint probabilities in a video. The evolution in a ST-JPI during various transitions falls into one of several well-deÿned linear patterns. Based on the patterns in a ST-JPI, our algorithm detects and classiÿes video transitions e ectively.Our study shows that temporal video segmentation based on ST-JPIs is distinguished from previous methods in the following way: (1) It is e ective and relatively robust not only for video cuts but also for gradual transitions; (2) It classiÿes transitions on the basis of predeÿned evolution patterns of ST-JPIs during transitions; (3) It is e cient, scalable and suitable for real-time video segmentation. Theoretical analysis and experimental results of our method are presented to illustrate its e cacy and e ciency. ?

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Metrics for shot boundary detection in digital video sequences

Cited by 55 publications

References 18 publications

Shot Boundary Detection for a Video under the Influence of Illumination: A Adaptive Thresholding Approach

Shot Boundary Detection for a Video under the Influence of Illumination: A Adaptive Thresholding Approach

Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

Spatio-temporal joint probability images for video segmentation

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