&lt;title&gt;Temporal segmentation of videos: a new approach&lt;/title&gt;

Cherfaoui, Mourad; Bertin, Christian

doi:10.1117/12.206380

Cited by 9 publications

(4 citation statements)

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“…Cherfaoui and Bertin [20] detect shot changes in two steps. First the global motion parameters are estimated using an affine transformation model.…”

Section: Global Motionmentioning

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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“…Cherfaoui and Bertin [20] detect shot changes in two steps. First the global motion parameters are estimated using an affine transformation model.…”

Section: Global Motionmentioning

confidence: 99%

“….95 Joint probability images [55] O(2P + N 2 + N ) Hidden Markov Model with 3 features [26] O(13P + 11N ) [20,16,93,60] O(20P) + 80.80 Motion vectors [8,75,58] O(75P) + 2312.73 Optical flow [28] O(105P) + Frequency domain correlation [68] O(75P) + 105.65 [33] O (4P + 3N ) 16.47…”

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confidence: 99%

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

2003

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“…These key frames can later be used for the actual retrieval process. The scene change detection can be done by using the decompressed video data [4][5][6][7] (e.g., color histogram-based methods). Decompression of video data is a costly operation.…”

Section: Related Workmentioning

confidence: 99%

VORTEX: Video Retrieval and Tracking from Compressed Multimedia Databases—Multiple Object Tracking from MPEG-2 Bit Stream

Schonfeld

Lelescu

2000

Journal of Visual Communication and Image Representation

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“…Cherfaoui [5] have suggested the shot motion classification scheme that computes global motion parameters for the camera following an optical flow computation scheme. An example of block matching based motion classification scheme is the work of Zhang et al [6] that carry out motion classification by computing the direction of motion vectors and the point of convergence or divergence.…”

Section: Introductionmentioning

confidence: 99%

<title>Flow estimation for motion analysis on compressed domain</title>

Kim

Choi

2003

Storage and Retrieval Methods and Applications for Multimedia 2004

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Because video sequences consist of dynamic video objects in nature, video object motion is an effective feature in describing the content of video sequences. In this paper, we propose a method that converts motion vectors (MVs) on MPEG coded domain to a uniform set, independent of the frame type and the direction of prediction, and directly utilizes these normalized MVs (N-MVs) in understanding video contents. To obtain such uniform MV set, we proposed a new motion analysis method using Bi-directional Prediction-Independent Framework. Generally, it is impossible to directly compare an I frame without MV to other frame types such as B or P frame. But, our approach enables a frame-type independent representation that normalizes temporal features including frame type, MB encoding and MVs. In the experiments, we show that the proposed method is better than the conventional one in terms of performance.

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<title>Temporal segmentation of videos: a new approach</title>

Cited by 9 publications

References 0 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

VORTEX: Video Retrieval and Tracking from Compressed Multimedia Databases—Multiple Object Tracking from MPEG-2 Bit Stream

<title>Flow estimation for motion analysis on compressed domain</title>

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