BilVideo: A video database management system

1996

Modeling moving objects has become a topic of increasing interest in the area of video databases. Two key aspects of such modeling are object spatial and temporal relationships. In this paper we introduce an innovative way to represent the trajectory of single moving object and the relative spatio-temporal relations between multiple moving objects. The representation supports a rich set of spatial topological and directional relations. It also supports both quantitative and qualitative user queries about moving objects. Algorithms for matching trajectories and spatio-temporal relations of moving objects are designed to facilitate query processing. These algorithms can handle both exact and similarity matches. We also discuss the integration of our moving object model, based on a video model, in an object-oriented system. Some query examples are provided to further validate the expressiveness of our model.

Proceedings of International Workshop on Multimedia Database Management Systems

A key aspect in video modeling is spatial relationships. In this paper we propose a spatial representation for specifying the spatial semantics of video data. Based on such a representation, a set of spatial relationships for salient objects is defined to support qualitative and quantitative spatial properties. The model captures both topological and directional spatial relationships. We present a novel way of incorporating this model into a video model, and integrating the abstract video model into an object database management system which has rich multimedia temporal operations. The integrated model is further enhanced by a spatial inference engine. The powerful expressiveness of our video model is validated by some query examples.

2004

In this thesis, we have extended a video database management system, called BilVideo, with semantic querying capability. Our work is based on a video data model for the extraction and storage of the semantic contents of videos, and a query language to support semantic queries on video data. The Web based query interface of BilVideo has also been modified to handle semantic queries both visually and textually.

Lecture Notes in Computer Science, 2000

A constraint of existing content-based video data models is that each modeled semantic description must be associated with time intervals exactly within which it happens and semantics not related to any time interval are not considered. Consequently, users are provided with limited query capabilities. This paper is aimed at developing a novel model with two innovations: (1) Semantic contents not having related time information can be modeled as ones that do (2) Not only the temporal feature of semantic descriptions, but also the temporal relationships among themselves are components of the model. The query system is by means of reasoning on those relationships. To support users' access, a video algebra and a video calculus as formal query languages, which are based on semantic relationship reasoning, are also presented.

Workshop on Multimedia Information Systems, 2002

The most useful environments for advancing research and development in video databases are those that provide complete video database management, including (1) video preprocessing for content representation and indexing, (2) storage management for video, metadata and indices, (3) image and semantic -based query processing, (4) real- time buffer management, and (5) continuous media streaming. Such environments support the entire process

Lecture Notes in Computer Science, 2000

Modeling video data poses a great challenge since they do not have as clear an underlying structure as traditional databases do. We propose a graphical object-based model, called VideoGraph, in this paper. This scheme has the following advantages: (1) In addition to semantics of video individual events, we capture their temporal relationships as well. (2) The inter-event relationships allow us to deduce implicit video information. (3) Uncertainty can also be handled by associating the video event with a temporal Boolean-like expression. This also allows us to exploit incomplete information. The above features make VideoGraph very exible in representing various metadata types extracted from diverse information sources. To facilitate video retrieval, we a l s o i n troduce a formalism for the query language based on path expressions. Query processing involves only simple traversal of the video graphs.

Lecture Notes in Computer Science, 2000

In this paper, we propose a new approach for high level segmentation of a video clip into shots using spatio-temporal relationships between objects in video frames. The technique we use is simple, yet novel and powerful in terms of effectiveness and user query satisfaction. Video clips are segmented into shots whenever the current set of relations between objects changes and the video frames where these changes have occurred are chosen as key frames. The topological and directional relations used for shots are those of the key frames that have been selected to represent shots and this information is kept, along with key frame intervals, in a knowledge-base as Prolog facts. We also have a comprehensive set of inference rules in order to reduce the number of facts stored in our knowledge-base because a considerable number of facts, which otherwise would have to be stored explicitly, can be derived by these rules with some extra effort.

2002

Abstract In this paper, we present an efficient video data model to represent moving trajectories of video objects and spatiotemporal relationships among the video objects. A video clip is segmented into a set of common appearance intervals (CAIs). A CAI is a time interval that video objects appear together. Transitions among CAIs record the appearance/disappearance of video objects. Depending on the properties of video objects, they are classified as foreground and background video objects.

Multimedia Computing and Networking 1997, 1997

One of the key aspects of videos is the temporal relationship between video frames. In this paper we propose a tree-based model for specifying the temporal semantics of video data. We present a unique way o f i n tegrating our video model into an object database management system which has rich m ultimedia temporal operations. We further show h o w temporal histories are used to model video data, explore the video objectbase using object-oriented techniques. Such a seamless integration gives a uniform interface to end users. The integrated video objectbase management system supports a broad range of temporal queries.

1995

1063-6382/95 $4.00 0 1995 IEEE

IEICE Transactions on Information and Systems

Recently, two approaches investigated indexing and retrieving videos. One approach utilized the visual features of individual objects, and the other approach exploited the spatio-temporal relationships between multiple objects. In this paper, we integrate both approaches into a new video model, called the Visual-Spatio-Temporal (VST) model to represent videos. The visual features are modeled in a topological approach and integrated with the spatio-temporal relationships. As a result, we defined rich sets of VST relationships which support and simplify the formulation of more semantical queries. An intuitive query interface which allows users to describe VST features of video objects by sketch and feature specification is presented. The conducted experiments prove the effectiveness of modeling and querying videos by the visual features of individual objects and the VST relationships between multiple objects.

Proceedings of the Eleventh International Conference on Data Engineering

In this paper, we propose a graphical data model for specifying spatio-temporal semantics of video data. The proposed model segments a video clip into subsegments consisting of objects. Each object is detected and recognized, and the relevant information of each object is recorded. The motions of objects are modeled through their relative spatial relationships as time evolves. Based on the semantics provided b y this model, a user can create his/her own object-oriented view of the video database. Using ihe propositional logic, we describe a methodology f o r specifying conceptual queries involving spatio-temporal semantics and expressing views f o r retrieving various video clips. Alternatively, a user can sketch the query, by examplifying the concept. The proposed methodology can be used to specify spatio-temporal concepts at various levels of information granularity.

Proceedings of the seventh ACM international conference on Multimedia (Part 1), 1999

Multimedia databases have been the subject of extensive research for the last several years. In particular, semantic modeling of video data spurred tremendous interest and produced various formalisms for content-based retrieval of video data. In this paper, we propose the use of Hierarchical Petri-nets (HPN's) for multi-level representation of video data with each level consisting of an augmented Petrinet structure. We show how such representation is able to capture video data semantics, from very low level semantics such as scene change and object movements to higher level semantics involving highlevel textual description of events. A Petri-net based method for query processing of multi-object spatiotemporal video-data queries is presented.

1997

abstract One of the key aspects of videos is the temporal relationship between video frames. In this paper we propose a tree-based model for specifying the temporal semantics of video data. We present a unique way of integrating our video model into an object database management system which has rich multimedia temporal operations. We further show how temporal histories are used to model video data, explore the video object base using object-oriented techniques.

2001

Video database and video on demand represent important applications of the evolving Global Information Infrastructure. Conventional database management systems (DBMS) are not well-suited for video querying and retrieval because they are designed to operate on alphanumeric data where selection conditions are easier to specify. Defining similarity between video data is difficult since the similarity involves semantics of the video, which is inherent in the data itself. We are planning to integrate two approaches to modeling, indexing and querying video data. The first one is based on textual annotations and the second is based on object motion and digital video processing techniques, in order to support content based and intelligent access. In addition to this novel data model, we also plan to develop a s ystem prototype that can synergistically use multiple media modes to retrieve data using fuzzy information fusion.