Projection detecting filter for video cut detection

Otsuji, Kiyotaka; Tonomura, Yoshinobu

doi:10.1145/166266.166295

Cited by 65 publications

(19 citation statements)

References 2 publications

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“…The last feature used is linked to spatio-temporal volumes. These volumes are built thanks to the projection of the video data along the (x, t) and the (y, t) planes, as in [67]. For a given sequence, edges perpendicular to the t axis may indicate shot changes.…”

Section: Combination Of Several Methodsmentioning

confidence: 99%

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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We present in this paper a review of methods for segmentation of uncompressed video sequences. Video segmentation is usually performed in the temporal domain by shot change detection. In case of real-time segmentation, computational complexity is one of the criteria which has to be taken into account when comparing different methods. When dealing with uncompressed video sequences, this criterion is even more significant. However previous published reviews did not involve complexity criterion when comparing shot change detection methods. Only recognition rate and ability to classify detected shot changes were considered. So contrary to previous reviews we give here complexity of most of the described methods. We review in this paper an extensive set of methods presented in the literature and classify them in several parts, depending on the information used to detect shot changes. The earliest methods were comparing successive frames by relying on most simple elements, that is to say pixels. Comparison could be performed on a global level, so methods based on histograms were also proposed. Block-based methods have been considered to process data at an intermediate level, between local (using pixels) and global (using histograms) levels. More complex features can be involved, resulting in feature-based methods. Alternatively some methods rely on motion as a criterion to detect shot changes. Finally different kinds of information could be combined together in order to increase the quality of shot change detection. So our review will detail segmentation methods

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Section: Combination Of Several Methodsmentioning

confidence: 99%

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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“…That is, such semantic contents themselves are not extracted from video data, but the editorial technique is extracted to evaluate the semantic contents. Prior to the extraction of scene features, the process of cut detection [37] or extraction of scene unit [48] is mandatory for automated parsing of video data.…”

Section: Cbr By Derivation Knowledgementioning

confidence: 99%

A survey on content-based retrieval for multimedia databases

Adachi

Ichikawa

1999

IEEE Trans. Knowl. Data Eng.

277

123

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Abstract-Conventional database systems are designed for managing textual and numerical data, and retrieving such data is often based on simple comparisons of text/numerical values. However, this simple method of retrieval is no longer adequate for the multimedia data, since the digitized representation of images, video, or data itself does not convey the reality of these media items. In addition, composite data consisting of heterogeneous types of data also associates with the semantic content acquired by a user's recognition. Therefore, content-based retrieval for multimedia data is realized taking such intrinsic features of multimedia data into account. Implementation of the content-based retrieval facility is not based on a single fundamental, but is closely related to an underlying data model, a priori knowledge of the area of interest, and the scheme for representing queries. This paper surveys recent studies on content-based retrieval for multimedia databases from the point of view of three fundamental issues. Throughout the discussion, we assume databases that manage only nontextual/numerical data, such as image or video, are also in the category of multimedia databases.

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“…Existing work has relied directly on intensity data, using such t e c hniques as image di erencing and intensity histogramming. Most approaches are based on intensity histograms, and concentrate on cuts 6,7] These methods have di culty with \busy" scenes, in which i n tensities change substantially from frame to frame. Such c hanges often result from camera or object motion.…”

Section: Scene Break Detectionmentioning

confidence: 99%

Video browsing using edges and motion

Zabih

Miller

Mai

1996

Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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Automatic video b r owsing requires algorithms for detecting a variety of events, including production effects (e.g., scene breaks and captions) and moving objects. We present new methods that use edges and motion for detecting production e ects and computing motion segmentation. Production e ects, such as cuts, dissolves, wipes and captions, can be detected b y l o oking for new edges that are f a r f r om previous edges. A global motion computation is used t o r egister consecutive images. We have also developed a method for motion segmentation, which does not require c omputing local optical ow. Our methods run at several frames per second on a Sparc workstation, and tolerate compression artifacts.

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Projection detecting filter for video cut detection

Cited by 65 publications

References 2 publications

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

A survey on content-based retrieval for multimedia databases

Video browsing using edges and motion

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