A Video Shot Boundary Detection Algorithm Based on Feature Tracking

Gao, Xinbo; Li, Jie; Shi, Yang

doi:10.1007/11795131_95

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…Hanjalic [13] defines the problem of Shot Boundary Detection (SBD) as finding discontinuities in the feature distance between frames. Many different feature types have been used for SBD including: Pixel differences [35], Colour histograms [20], tracking of features [10] and mutual information [5]. Yuan et al [34] try to provide a formal framework for SBD and review many existing techniques while suggesting optimal criteria for the different sections of the framework.…”

Section: Related Workmentioning

confidence: 99%

Friendly Faces: Weakly Supervised Character Identification

Marter

Hadfield

Bowden

2015

Face and Facial Expression Recognition From Real World Videos

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Abstract. This paper demonstrates a novel method for automatically discovering and recognising characters in video without any labelled examples or user intervention. Instead weak supervision is obtained via a rough script-to-subtitle alignment. The technique uses pose invariant features, extracted from detected faces and clustered to form groups of co-occurring characters. Results show that with 9 characters, 29% of the closest exemplars are correctly identified, increasing to 50% as additional exemplars are considered.

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Section: Related Workmentioning

confidence: 99%

Friendly Faces: Weakly Supervised Character Identification

Marter

Hadfield

Bowden

2015

Face and Facial Expression Recognition From Real World Videos

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“…Image features used for VSBD include color feature [1,2] , edge change ratio [3] , corner points [4] , Scale Invariant Feature Transform(SIFT) [5] , as well as the combination of different features [1,6,7] etc. Color feature has less computational cost than other features, but this feature is highly sensitive to camera motion and noise.…”

Section: Introductionmentioning

confidence: 99%

Video shot boundary detection based on MB coding mode and SIFT features on H.264/AVC

Zhang¹

2014

2014 IEEE International Conference on Progress in Informatics and Computing

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To segment video shot quickly and efficiently, a video shot boundary detection algorithm which combine Macro Block (MB) coding mode with Scale Invariant Feature Transform(SIFT) feature point matching is proposed in this paper. Firstly, the MB coding mode is extracted from H.264/AVC bit stream to calculate the intra coding MB ratio of a frame, according to intra coding MB ratio, some frames are selected out as candidate shot boundary frame; Secondly, the matching ratio of SIFT feature point matching of two consecutive candidate frames is calculated to exclude non-shot boundary frame and retain shot boundary frame. The experimental results indicate that proposed algorithm performs well on both cut and gradual shot boundary detection.

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“…The features used for shot boundary detection include color histogram [87] or block color histogram, edge change ratio, motion vectors [85], [163], together with more novel features such as scale invariant feature transform [83], corner points [82], information saliency map [77], etc. Color histograms are robust to small camera motion, but they are not able to differentiate the shots within the same scene, and they are sensitive to large camera motions.…”

Section: A Shot Boundary Detectionmentioning

confidence: 99%

A Survey on Visual Content-Based Video Indexing and Retrieval

Xie

et al. 2011

IEEE Trans. Syst., Man, Cybern. C

469

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Abstract-Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.Index Terms-Feature extraction, video annotation, video browsing, video retrieval, video structure analysis.

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A Video Shot Boundary Detection Algorithm Based on Feature Tracking

Cited by 9 publications

References 11 publications

Friendly Faces: Weakly Supervised Character Identification

Friendly Faces: Weakly Supervised Character Identification

Video shot boundary detection based on MB coding mode and SIFT features on H.264/AVC

A Survey on Visual Content-Based Video Indexing and Retrieval

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