Object-based video indexing for virtual-studio productions

Günsel, Bilge; Tekalp, A. Murat; Beek, P.J.L. van

doi:10.1109/cvpr.1997.609413

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…33 The proposed system has some limitations: It is difficult to track occluded and/or transparent semantic objects; furthermore, the scene classification algorithm may confuse an anchorperson shot with a news clip during an interview if the interviewed person occupies the same spatial location as an anchorperson. This two-class clustering concept may also be applied for clustering of motion or shape similarities.…”

Section: Discussionmentioning

confidence: 99%

Temporal video segmentation using unsupervised clustering and semantic object tracking

Günsel¹,

Ferman

Tekalp

1998

J. Electron. Imaging

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This paper proposes a content-based temporal video segmentation system that integrates syntactic (domainindependent) and semantic (domain-dependent) features for automatic management of video data. Temporal video segmentation includes scene change detection and shot classification. The proposed scene change detection method consists of two steps: detection and tracking of semantic objects of interest specified by the user, and an unsupervised method for detection of cuts, and edit effects. Object detection and tracking is achieved using a region matching scheme, where the region of interest is defined by the boundary of the object. A new unsupervised scene change detection method based on two-class clustering is introduced to eliminate the data dependency of threshold selection. The proposed shot classification approach relies on semantic image features and exploits domain-dependent visual properties such as shape, color, and spatial configuration of tracked semantic objects. The system has been applied to segmentation and classification of TV programs collected from different channels. Although the paper focuses on news programs, the method can easily be applied to other TV programs with distinct semantic structure.

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Section: Discussionmentioning

confidence: 99%

Temporal video segmentation using unsupervised clustering and semantic object tracking

Günsel¹,

Ferman

Tekalp

1998

J. Electron. Imaging

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“…Some indexing algorithms use objects and their attributes, as well as spatial and/or temporal relations among objects in a video to label and index video sequences [15]- [17]. For example, Gunsel et al [17] introduced an approach to temporal video partitioning and content-based video indexing, in which the basic indexing unit was "life-span of a video object, rather than a camera shot or story unit."…”

Section: Related Work On Video Indexing and Retrievalmentioning

confidence: 99%

“…For example, Gunsel et al [17] introduced an approach to temporal video partitioning and content-based video indexing, in which the basic indexing unit was "life-span of a video object, rather than a camera shot or story unit." They indexed motion and shape information of video object planes tracked at each frame and provided an object-based access to video data.…”

Section: Related Work On Video Indexing and Retrievalmentioning

confidence: 99%

A Natural Language Approach to Content-Based Video Indexing and Retrieval for Interactive E-Learning

Zhang

Nunamaker

2004

IEEE Trans. Multimedia

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As a powerful and expressive nontextual media that can capture and present information, instructional videos are extensively used in e-Learning (Web-based distance learning). Since each video may cover many subjects, it is critical for an e-Learning environment to have content-based video searching capabilities to meet diverse individual learning needs. In this paper, we present an interactive multimedia-based e-Learning environment that enables users to interact with it to obtain knowledge in the form of logically segmented video clips. We propose a natural language approach to content-based video indexing and retrieval to identify appropriate video clips that can address users' needs. The method integrates natural language processing, named entity extraction, frame-based indexing, and information retrieval techniques to explore knowledge-on-demand in a video-based interactive e-Learning environment. A preliminary evaluation shows that precision and recall of this approach are better than those of the traditional keyword based approach.Index Terms-Learning by asking, natural language processing, video indexing and retrieval.

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“…Nagasaka and Tanaka presented full-video searching technology for specified objects using features derived locally [19]. An object-based approach for temporal video partitioning and content-based indexing was presented in [11].…”

Section: Related Workmentioning

confidence: 99%

Wavelet-based video indexing and querying

Wen

Huffmire

et al. 1999

Multimedia Systems

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We present several algorithms suitable for analysis of broadcast video. First, we show how wavelet analysis of frames of video can be used to detect transitions between shots in a video stream, thereby dividing the stream into segments. Next we describe how each segment can be inserted into a video database using an indexing scheme that involves a wavelet-based "signature." Finally, we show that during a subsequent broadcast of a similar or identical video clip, the segment can be found in the database by quickly searching for the relevant signature. The method is robust against noise and typical variations in the video stream, even global changes in brightness that can fool histogram-based techniques. In the paper, we compare experimentally our shot transition mechanism to a color histogram implementation, and also evaluate the effectiveness of our databasesearching scheme. Our algorithms are very efficient and run in realtime on a desktop computer. We describe how this technology could be employed to construct a "smart VCR" that was capable of alerting the viewer to the beginning of a specific program or identifying commercials and then muting the volume on the TV.

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Object-based video indexing for virtual-studio productions

Cited by 13 publications

References 11 publications

Temporal video segmentation using unsupervised clustering and semantic object tracking

Temporal video segmentation using unsupervised clustering and semantic object tracking

A Natural Language Approach to Content-Based Video Indexing and Retrieval for Interactive E-Learning

Wavelet-based video indexing and querying

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