Temporal video segmentation: A survey

Koprinska, Irena; Carrato, Sergio

doi:10.1016/s0923-5965(00)00011-4

Cited by 324 publications

(169 citation statements)

References 31 publications

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“…Shot change detection is one of these techniques. Koprinska et al [48] review algorithms for shot change detection and camera operation recognition. Lienhart [57] presents the different kinds of shot changes and some dedicated detection methods.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…The number of shot change detection methods is now important and several reviews of these methods have been done [3,42,17,7,48,57,44,14,56,22,88,31,32,59]. These reviews often present the different methods and their efficiency based on some quality measures.…”

Section: Introductionmentioning

confidence: 99%

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

2003

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“…Much work exists in shot-cut detection [2,3]. Popular techniques that use image features [7], optical flow [14] etc., are not applicable due to the heavy noise that is common in broadcast videos.…”

Section: Visual Domain Processing Of Videosmentioning

confidence: 99%

“…• Shot-Cut detection, using visual features, has been well addressed in literature [2,3,4]. However, the video shot obtained from cut detection is not generally a meaningful entity.…”

Section: Introductionmentioning

confidence: 99%

Text Driven Temporal Segmentation of Cricket Videos

Sankar

Pandey

Jawahar

2006

Computer Vision, Graphics and Image Processing

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Abstract. In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional topdown information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.

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“…Selected key-frames then represent each shot. There are different techniques to this aim [9], and the majority of proposed algorithms process uncompressed video, as we do. As to the selection of the appropriate algorithm to be used within our framework, we had to consider both speed and efficiency.…”

mentioning

confidence: 99%

Presentation-Oriented Key-Frames Coding Based on Fractals

Atzori¹,

Giusto²,

Murroni³

2005

ETRI J

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This paper focuses on the problem of key-frames coding and proposes a new promising approach based on the use of fractals. The summary, made of a set of key-frames selected from a full-length video sequence, is coded by using a 3D fractal scheme. This allows the video presentation tool to expand the video sequence in a "natural" way by using the property of the fractals to reproduce the signal at several resolutions. This feature represents an important novelty of this work with respect to the alternative approaches, which mainly focus on the compression ratio without taking into account the presentation aspect of the video summary. In devising the coding scheme, we have taken care of the computational complexity inherent in fractal coding. Accordingly, the key-frames are first wavelet transformed, and the fractal coding is then applied to each subband to reduce the search range. Experimental results show the effectiveness of the proposed approach.Keywords: Fractals, wavelet, video processing, multimedia. Manuscript received Sept. 19, 2003; revised Oct. 18, 2005. Luigi Atzori (phone: + 39 070 675 5902, email: l.atzori@diee.unica.it ), Daniele D. Giusto (email: ddgiusto@unica.it), and Maurizio Murroni (email: murroni@diee.unica.it) are with the Department of Electrical and Electronic Engineering, University of Cagliari, Italy. I. IntroductionThe surprising diffusion of multimedia applications (from scientific to commercial, and from informative to recreational) over heterogeneous networks has caused a great deal of interest in the scientific community toward signal processing and data transmission fields. In most of these applications, digital video archives are browsed on distributed networks that are subject to buffer congestions and bandwidth constraints. To enable these services, it is important to develop tools to analyze and describe the video content, handle queries from the end-users, and provide results. These operations require the extraction of the essence of the visual content in a compact form so as to permit a fast browsing of huge multimedia archives. Accordingly, a procedure for automatic video data analysis and indexing has become a requirement for efficient database content searching and management. It is mainly made up of the following tasks [1]: feature extraction, structure analysis, abstraction, and indexing. The first task is aimed at providing the major characteristics of the video (such as color, texture, shape, structure, layout, and motion) that can be converted into semantic concepts. Video structure parsing is the next step in overall video-content analysis and is the process of extracting temporal structural information of video sequences or programs. Video abstraction is the process of creating a presentation of visual information about a landscape or the structure of video, which should be much shorter than the original video. Based on the output of the previous tasks, video indices are built so as to enable a fast browsing of the visual content.Researchers have extensively investi...

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Temporal video segmentation: A survey

Cited by 324 publications

References 31 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

Text Driven Temporal Segmentation of Cricket Videos

Presentation-Oriented Key-Frames Coding Based on Fractals

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