MultiView: Multilevel video content representation and retrieval

Fan, Jianping; Aref, Walid G.; Elmagarmid, Ahmed K.; Hacid, Mohand-Saïd; Marzouk, Mirette; Zhu, Xingquan

doi:10.1117/1.1406944

Cited by 39 publications

(24 citation statements)

References 51 publications

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“…It supports fuzzy spatial queries including querying spatial relationships between objects. Multilevel video database model provides a reasonable approach to bridging the gap between low-level representation features and high level semantic concepts from a human point of view [14] [15]. A hierarchical semantics, sensitive video classifier is proposed to shorten the semantic gap between the low-level visual features and high level semantic concepts.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

Nikam¹,

Nandwalkar²

2015

IJCA

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Video based applications disclosed need for efficiently extracting and modeling the video contents. The video features can be classified into normal data, relative features and logic content. Semantic level understanding is required for core content of video. So to get video content automatic semantic content framework is proposed. In proposed system a semantic content extraction system that allows the user to query and regain objects, events, and concepts that are extracted automatically. VISCOM is a video semantic content model which contains classes and relations between classes. Objects and events are represented by some VISCOM classes and other classes are used in the automatic semantic content extraction framework. VISCOM classes collect the semantic content types and relations.Ontology based fuzzy video data semantic model which uses spatial and temporal relations in event and concept definition is proposed. Extracted objects from consecutive representative frames are processed to extract temporal relations. Additional rules to lower spatial relation computation cost and to define some difficult situations more successfully are used. To extract objects from video we apply k-means clustering algorithm. By, which we get the more relevant objects related to user query.

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Section: Literature Surveymentioning

confidence: 99%

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

Nikam¹,

Nandwalkar²

2015

IJCA

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“…Most existing content-based video retrieval systems use one of two widelyaccepted approaches to access videos in a database [Flickner et al 1995;Pentland et al 1996;Rui et al 1998;Humrapur et al 1997;Chang et al 1998;Satoh and Kanade 1997;Deng and Manjunath 1998;Fan et al 2001a]: shot-based and object-based. These video retrieval systems, however, only focus on how to obtain these video access units (i.e., video shots and video objects), whereas few of them reveal or publish their video database models and indexing structures.…”

Section: Hierarchical Video Database Modelmentioning

confidence: 99%

“…However, this approach suffers from the problem of curse of dimensionality because the visual features used for video representation are normally in high dimensions. One reasonable solution to this problem is first to classify videos into a set of clusters and then to perform the dimensionality reduction on these clusters independently [Fan et al 2001a;Zhang et al 1997]. Traditional database indexing trees can supposedly be used for indexing these video clusters independently, with relatively low-dimensional features.…”

Section: Hierarchical Video Database Modelmentioning

confidence: 99%

“…In recent years, there has been a growing interest in developing more effective methods for retrieving and browsing video databases over networks. Several content-based video database systems have been proposed, such as QBIC, VideoQ, MARS, Netra-V, Virage, Photobook, Name-It, and MultiView [Flickner et al 1995;Pentland et al 1996;Rui et al 1998;Humrapur et al 1997;Chang et al 1998;Satoh and Kanade 1997;Deng and Manjunath 1998;Fan et al 2001a]. In order to support video retrieval, most of those systems first partition videos into a set of access units such as shots, objects, or regions, and then follow the approach of representing video via a set of feature attributes, such as color, texture, shape, and layout [Fan et al 2001a;Zhang et al 1997;Jain et al 1999;Tamura et al 1978].…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical access control model for video database systems

Bertino

Fan

Ferrari

et al. 2003

ACM Trans. Inf. Syst.

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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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“…Multilevel models constitute a possible approach toward the solution of this problem. An advantage of these models is that they provide a reasonable way to bridge the gap between lowlevel visual features and high level semantic concepts [16].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Population and Query Interface for a Content-Based Video Database

Ardizzone¹,

Cascia²,

Maniscalco

et al. 2002

XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops

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Abstract. In this paper we describe the first full implementation of a contentbased indexing and retrieval system for MPEG-2 and MPEG-4 videos. We consider a video as a collection of spatiotemporal segments called video objects; each video object is a sequence of video object planes. A set of representative video object planes is used to index each video object. During the database population, the operator, using a semi-automatic outlining tool we developed, manually selects video objects and insert some semantical information. Lowlevel visual features like color, texture, motion and geometry are automatically computed. The system has been implemented on a commercial relational DBMS and is based on the client server paradigm. The database population client is a stand-alone Windows application; the querying of the system is performed with a web-based client. To test the system we indexed a number of videos taken from Italian soccer championship games. A few examples of queries in this particular domain are reported and results appear very promising.

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MultiView: Multilevel video content representation and retrieval

Cited by 39 publications

References 51 publications

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

A hierarchical access control model for video database systems

Population and Query Interface for a Content-Based Video Database

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