A survey on content-based retrieval for multimedia databases <s> CBR by Descriptive Knowledge <s> The goal of the VIMSYS project is to create an information management system that can retrieve images or parts of images in addition to textual data. The system allows users to incrementally formulate a query starting from semantic pictorial objects. 11 allows incompletely specified and similarity-based queries. This paper presents a new layered data model and the design of the query processing unit for this system. The data model is a combination of object-oriented and functional models, and permits multiple representations of image entities. The knowledge module guides the user to progrcssively refine similarity-based queries and to query by defining new semantic objects and composing its attributes. Design considerations for the access structure of the system are also discussed, alongwith some of the problems of creating efficient mecha- <s> BIB001 </s> A survey on content-based retrieval for multimedia databases <s> CBR by Descriptive Knowledge <s> Video-on-demand represents a key demonstrative application for enabling multimedia technology in communication, database, and interface research. This application requires solving a number of diverse technical problems including the data synchronization problem for time-dependent data delivery. In this paper we describe the general requirements of video-on-demand and introduce a system supporting content-based retrieval and playback for the structure and content of digital motion pictures. In our model we capture domain-specific information for motion pictures and provide access to individual scenes of movies through queries on a temporal database. We describe our implementation of this service using existing workstation and storage technology. <s> BIB002 </s> A survey on content-based retrieval for multimedia databases <s> CBR by Descriptive Knowledge <s> A video-object data model and the design and implementation of a prototype video-object database system named OVID based on the model are described. Notable features of the video-object data model are a mechanism to share common descriptional data among video-objects, called interval-inclusion based inheritance, and operations to composite video-objects. The OVID system offers a browsing/inspection tool called VideoChart, and adhoc query facility called VideoSQL, and a video-object definition tool. > <s> BIB003 </s> A survey on content-based retrieval for multimedia databases <s> CBR by Descriptive Knowledge <s> Algebraic video uses a set of basic operations on video segments to create a desired video stream. The video algebra consists of operations for temporally and spatially combining video segments, and for attaching attributes to these segments. Video streams of interest can be discovered with video queries that describe desired attributes. Unlike previous approaches, algebraic video permits video expressions to be nested in arbitrarily deep hierarchies. It also permits video segments to inherit attributes by context. Experience with a prototype algebraic video system suggests that algebraic video can be used to create new video presentations from existing video libraries with query based discovery and algebraic combination of video segments of interest. > <s> BIB004
In 'query-by-subject,' a keyword representing a semantic content is specified. The semantic content implied in multimedia data is extracted from raw data in order to evaluate a query. A simple way of managing the semantic content of multimedia data is to annotate an image, a video or an audio data with text. Retrieving the contents of an image by referring to the annotation is described in  ,  ,  , BIB001 . Klinger and Pizano  assumes multimedia geographical information, where geographical entities in a map such as cities and roads are defined together with a textual description representing names of the cities or the roads, based on a kind of ER model. Examples that provide content-based retrieval for a video database through textual annotation are BIB004 , BIB002 , BIB003 . In these studies, a textual description representing semantic contents is assumed to be defined for an image or a video data by a human. These studies focus on 'retrieval-by-content' which cannot be extracted from an image or a video data through image processing. Such kinds of information include, for example, the name of a road in a map, the name of a person appearing in a news video. In these systems, content-based retrieval for image/video data is internally replaced by a keyword retrieval for annotations. One of the advantages of this method is that it can easily be implemented. Another advantage is that misevaluation of contents will hardly occur, which is often one of the issues of content-based retrieval extracting contents directly from raw data such as video or audio. However, this approach is not practical especially in large multimedia databases.