Spatiotemporal segmentation for compact video representation

Fan, Jianping; Yu, Jun; Fujita, Gen; Onoye, Takao; Wu, Lide; Shirakawa, Isao

doi:10.1016/s0923-5965(00)00036-9

Cited by 28 publications

(13 citation statements)

References 29 publications

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“…The simplest method of parsing video data for efficient browsing, retrieval, and navigation is segmenting the continuous video sequence into physical shots [35,37,38] and then selecting a constant number of keyframes for each shot to depict its content [1,2,32,43,45,47]. Unfortunately, since the video shot is a physical unit, it is incapable of conveying independent scenario information.…”

Section: Video Content Structure Detectionmentioning

confidence: 99%

Exploring video content structure for hierarchical summarization

Zhu

Fan

et al. 2004

Multimedia Systems

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In this paper, we propose a hierarchical video summarization strategy that explores video content structure to provide the users with a scalable, multilevel video summary. First, video-shot-segmentation and keyframe-extraction algorithms are applied to parse video sequences into physical shots and discrete keyframes. Next, an affinity (self-correlation) matrix is constructed to merge visually similar shots into clusters (supergroups). Since video shots with high similarities do not necessarily imply that they belong to the same story unit, temporal information is adopted by merging temporally adjacent shots (within a specified distance) from the supergroup into each video group. A video-scene-detection algorithm is thus proposed to merge temporally or spatially correlated video groups into scenario units. This is followed by a scene-clustering algorithm that eliminates visual redundancy among the units.A hierarchical video content structure with increasing granularity is constructed from the clustered scenes, video scenes, and video groups to keyframes. Finally, we introduce a hierarchical video summarization scheme by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary. Extensive experiments based on real-world videos have been performed to validate the effectiveness of the proposed approach.

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Section: Video Content Structure Detectionmentioning

confidence: 99%

Exploring video content structure for hierarchical summarization

Zhu

Fan

et al. 2004

Multimedia Systems

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“…To this end, we use a contour-based temporal tracking procedure. 38 The procedure uses two semantic features, motion and contour, to establish object correspondence across frames. The kth Hausdorff distance technique is used to guarantee the temporal object tracking procedure.…”

Section: A Hybrid Image Segmentation Technique Integrates Thementioning

confidence: 99%

MultiView: Multilevel video content representation and retrieval

Fan

Aref

Elmagarmid

et al. 2001

J. Electron. Imaging

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Abstract. In this article, several practical algorithms are proposed to support content-based video analysis, modeling, representation, summarization, indexing, and access. First, a multilevel video database model is given. One advantage of this model is that it provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic concepts from a human point of view. Second, several model-based video analysis techniques are proposed. In order to detect the video shots, we present a novel technique, which can adapt the threshold for scene cut detection to the activities of variant videos or even different video shots. A seeded region aggregation and temporal tracking technique is proposed for generating the semantic video objects. The semantic video scenes can then be generated from these extracted video access units (e.g., shots and objects) according to some domain knowledge. Third, in order to categorize video contents into a set of semantic clusters, an integrated video classification technique is developed to support more efficient multilevel video representation, summarization, indexing, and access techniques.

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“…The physical video shots are too general to characterize the associated semantic visual concepts. Since the presence or absence of the salient objects (i.e., regions of interest) can indicate the presence or absence of the related semantic visual concepts [Fan et al 2001b[Fan et al , 2001c[Fan et al , 2001dWang and Chang 1999;Zhong et al 2000], salient objects are very attractive for characterizing the semantic visual concepts and supporting semantic video classification. (b) A semantic video classifier to shorten the semantic gap between the lowlevel visual features and the high-level semantic visual concepts.…”

Section: Hierarchical Video Database Modelmentioning

confidence: 99%

An access control model for video database systems

Bertino

Hammad

Aref

et al. 2000

Proceedings of the Ninth International Conference on Information and Knowledge Management

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Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel Permission to make digital/hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specific permission and/or a fee. 156• Bertino et al.video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.

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Spatiotemporal segmentation for compact video representation

Cited by 28 publications

References 29 publications

Exploring video content structure for hierarchical summarization

Exploring video content structure for hierarchical summarization

MultiView: Multilevel video content representation and retrieval

An access control model for video database systems

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