Performance characterization and comparison of video indexing algorithms

Gargi, Ullas; Kasturi, Rangachar

doi:10.1109/cvpr.1998.698661

Cited by 34 publications

(24 citation statements)

References 16 publications

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“…Extensive evaluation of various approaches has shown that simple thresholding of histogram distances performs surprisingly well and is difficult to beat [7], [17]. In this work, we consider an alternative formulation that regards the problem as one of statistical inference between two hypothesis:…”

Section: A Bayesian Framework For Shot Segmentationmentioning

confidence: 99%

“…Because shot segmentation is a pre-requisite for virtually any task involving the understanding, parsing, indexing, characterization, or categorization of video, the grouping of video frames into shots has been an active topic of research in the area of content-based retrieval [7], [17], [19], [30], [41], [53], [56]. Extensive evaluation of various approaches has shown that simple thresholding of histogram distances performs surprisingly well and is difficult to beat [7], [17].…”

Section: A Bayesian Framework For Shot Segmentationmentioning

confidence: 99%

“…The color histogram distance has been widely used as a measure of (dis)similarity between images for the purposes of object recognition [16], [42], content-based retrieval [23], [27], [31], [32], [40], and temporal video segmentation [7], [17], [30], [41], [53], [56]. A histogram is first computed for each image in the sequence and the distance between successive histograms is used as a measure of local activity.…”

Section: A Color Histogram Distancementioning

confidence: 99%

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Statistical models of video structure for content analysis and characterization

Vasconcelos

Lippman

2000

IEEE Trans. on Image Process.

108

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Abstract-Content structure plays an important role in the understanding of video. In this paper, we argue that knowledge about structure can be used both as a means to improve the performance of content analysis and to extract features that convey semantic information about the content. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models with two practical applications. First, we develop a Bayesian formulation for the shot segmentation problem that is shown to extend the standard thresholding model in an adaptive and intuitive way, leading to improved segmentation accuracy. Second, by applying the transformation into the shot duration/activity feature space to a database of movie clips, we also illustrate how the Bayesian model captures semantic properties of the content. We suggest ways in which these properties can be used as a basis for intuitive content-based access to movie libraries.Content structure plays an important role in the understanding of video. In this paper, we argue that knowledge about structure can be used both as a means to improve the performance of content analysis and to extract features that convey semantic information about the content. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models with two practical applications. First, we develop a Bayesian formulation for the shot segmentation problem that is shown to extend the standard thresholding model in an adaptive and intuitive way, leading to improved segmentation accuracy. Second, by applying the transformation into the shot duration/activity feature space to a database of movie clips, we also illustrate how the Bayesian model captures semantic properties of the content. We suggest ways in which these properties can be used as a basis for intuitive content-based access to movie libraries.

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Section: A Bayesian Framework For Shot Segmentationmentioning

confidence: 99%

Section: A Bayesian Framework For Shot Segmentationmentioning

confidence: 99%

Section: A Color Histogram Distancementioning

confidence: 99%

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Statistical models of video structure for content analysis and characterization

Vasconcelos

Lippman

2000

IEEE Trans. on Image Process.

108

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“…To detect dissolves, the accumulated difference over a range of pictures with successive histogram differences greater than the lower threshold, but not large enough to individually exceed the larger threshold, should exceed the larger threshold. A good comparative assessment of some of these approaches can be found in [6] and [7]. The comparison in [6] takes into account newer algorithms designed to detect more complex edits (fades and dissolves).…”

Section: Related Workmentioning

confidence: 99%

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

Lelescu¹,

Schonfeld

2003

IEEE Trans. Multimedia

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Abstract-The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. In this paper, a novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.

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“…These phases are required to be automatically segmented, in order to check the form of the players and to analyze any deficiencies. On the other hand, for video sequences, methods for cutting scenes have also been studied [2]. Those detect boundary points at which several kinds of camera break, i.e.…”

Section: Introductionmentioning

confidence: 99%