A Formal Study of Shot Boundary Detection

Yuan, Jinhui; Wang, Huiyi; Xiao, Lan; Zheng, Wujie; Li, Jianmin; Lin, Fang; Zhang, Bo

doi:10.1109/tcsvt.2006.888023

Cited by 302 publications

(194 citation statements)

References 55 publications

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“…The issue of segmenting a video into scenes sharing the same visual features, usually called shot detection, has been an important topic of video retrieval research throughout the last 20 years [41]. This goal is commonly achieved by detecting hard cuts and gradual transitions (such as fade-in/fade-out, wipe, and dissolve), a task which involves the computation of visual differences between consecutive frames and the subsequent application of criteria to declare the presence of a shot cut (see Fig.…”

Section: Fig 1 Tagging Videos Using Textual Labelsmentioning

confidence: 99%

SHIATSU: tagging and retrieving videos without worries

Bartolini

Patella

Romani

2011

Multimed Tools Appl

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The dramatic growth of video content over modern media channels (such as the Internet and mobile phone platforms) directs the interest of media broadcasters towards the topics of video retrieval and content browsing. Several video retrieval systems benefit from the use of semantic indexing based on content, since it allows an intuitive categorization of videos. However, indexing is usually performed through manual annotation, thus introducing potential problems such as ambiguity, lack of information, and non-relevance of index terms. In this paper, we present SHIATSU, a complete system for video retrieval which is based on the (semi-)automatic hierarchical semantic annotation of videos exploiting the analysis of visual content; videos can then be searched by means of attached tags and/or visual features. We experimentally evaluate the performance of SHIATSU on two different real video benchmarks, proving its accuracy and efficiency.

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Section: Fig 1 Tagging Videos Using Textual Labelsmentioning

confidence: 99%

SHIATSU: tagging and retrieving videos without worries

Bartolini

Patella

Romani

2011

Multimed Tools Appl

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“…Shot-boundaries are detected using Bayesian cost functions, by comparing original frame with the predicted frame, estimated using within video shot linear prediction model (WLPM) and dissolve linear prediction model (DLPM). Yuan et al present a unified shot boundary detection system based on graph partitioning model [5]. The representation of the visual content, the construction of the continuity signal, and the classification of continuity values are handled in this work.…”

Section: Related Workmentioning

confidence: 99%

“…Various shot-boundary detection algorithms have been proposed [1][2][3][4][5][6] and compared [7][8][9][10]; however, to the best of our knowledge, no shot-boundary detection algorithm specialized for CBCD is found in the literature. Our aim is to propose an automatic shot-boundary detection algorithm for the videos on which various transformations are applied.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy color histogram-based video segmentation

Küçüktunç

Güdükbay

Ulusoy

2010

Computer Vision and Image Understanding

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a b s t r a c tWe present a fuzzy color histogram-based shot-boundary detection algorithm specialized for contentbased copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

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“…The methods analyzed in [23] can be categorized into two major groups: i) methods based on histogram distances, and ii) methods based on variations of MPEG coefficients. A comprehensive study is given in [24] where a formal framework for evaluation is also developed. Other methods include those where scene segmentation is based on image mosaicking [25,26] or frames are segmented according to underlying subspace structure [27].…”

Section: Example 1: Video Segmentationmentioning

confidence: 99%

The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams

Sznaier

Camps

Özay

et al. 2010

Dynamics of Information Systems

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Summary. A major roadblock in taking full advantage of the recent exponential growth in data collection and actuation capabilities stems from the curse of dimensionality. Simply put, existing techniques are ill-equipped to deal with the resulting overwhelming volume of data. The goal of this chapter is to show how the use of simple dynamical systems concepts can lead to tractable, computationally efficient algorithms for extracting information sparsely encoded in multimodal, extremely large data sets. In addition, as shown here, this approach leads to non-entropic information measures, better suited than the the classical, entropy-based information theoretic measure, to problems where the information is by nature dynamic and changes as it propagates through a network where the nodes themselves are dynamical systems.

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A Formal Study of Shot Boundary Detection

Cited by 302 publications

References 55 publications

SHIATSU: tagging and retrieving videos without worries

SHIATSU: tagging and retrieving videos without worries

Fuzzy color histogram-based video segmentation

The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams

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