Video Database System Research Articles

SEMCOG: A hybrid object-based image and video database system and its modeling, language, and query processing

SEMCOG: A hybrid object-based image and video database system and its modeling, language, and query processing

Spatial and temporal content-based access to hypervideo databases

Providing content-based video query, retrieval and browsing is the most important goal of a video database management system (VDBMS). Video data is unique not only in terms of its spatial and temporal characteristics, but also in the semantic associations manifested by the entities present in the video. This paper introduces a novel video data model called Logical Hypervideo Data Model. In addition to multilevel video abstractions, the model is capable of representing video entities that users are interested in (defined as hot objects) and their semantic associations with other logical video abstractions, including hot objects themselves. The semantic associations are modeled as video hyperlinks and video data with such property are called hypervideo. Video hyperlinks provide a flexible and effective way of browsing video data. Based on the proposed model, video queries can be specified with both temporal and spatial constraints, as well as with semantic descriptions of the video data. The characteristics of hot objects' spatial and temporal relations and efficient evaluation of them are also discussed. Some query examples are given to demonstrate the expressiveness of the video data model and query language. Finally, we describe a modular video database system architecture that our web-based prototype is based on.

Scene change detection techniques for video database systems

Scene change detection (SCD) is one of several fundamental problems in the design of a video database management system (VDBMS). It is the first step towards the automatic segmentation, annotation, and indexing of video data. SCD is also used in other aspects of VDBMS, e.g., hierarchical representation and efficient browsing of the video data. In this paper, we provide a taxonomy that classifies existing SCD algorithms into three categories: full-video-image-based, compressed-video-based, and model-based algorithms. The capabilities and limitations of the SCD algorithms are discussed in detail. The paper also proposes a set of criteria for measuring and comparing the performance of various SCD algorithms. We conclude by discussing some important research directions.

WVTDB-a semantic content-based video database system on the World Wide Web

Describes the design and implementation of the WVTDB (Web-based VideoText DataBase) system that demonstrates our research on video data modeling, semantic content-based video querying and video database system architectures. The video data model of WVTDB is based on multi-level video data abstractions and annotation layering, thus allowing dynamic and incremental video annotation and indexing, multi-user view sharing and video data reuse. Users can query, retrieve and browse video data based on their semantic content descriptions and temporal constraints on the video segments. WVTDB employs a modular system architecture that supports distributed video query processing and subquery caching. Several techniques, such as video wrappers and lazy delivery, are also proposed specifically to address the network bandwidth limitations for this kind of Web-based system. We also address adaptivity, data access control and user profile issues.

Spatio-temporal indexing of vector quantized video sequences

Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. Vector quantization (VQ) is an efficient technique for low bit-rate image and video compression. In addition, the low complexity of the decoder makes VQ attractive for low power systems and applications which require fast decoding. In this paper, we present an indexing technique for compressed video using vector quantization. Here, a video sequence is first compressed using VQ. Each frame is represented by a usage map, a set of VQ labels, and a set of motion vectors. The video sequence is partitioned into shots and the various camera operations and motion within each shot are then determined by processing the VQ label maps. Each shot is indexed using a spatio-temporal index. The spatial index refers to the spatial content of the representative frame of a shot, while the temporal index represents the temporal content of the shot. The spatial index is based on the codewords used to compress the representative frame, while the temporal index is based on motion and camera operations within the shot. The proposed indexing technique is executed entirely in the compressed domain. This entails significant savings in computational and storage costs resulting in faster execution.

Image and Video Databases: Visual Browsing, Querying and Retrieval

Image and video database systems have grown in popularity in the last few years due to the increased interest in and success of multimedia applications. As large bases of images are being made available to users, it becomes of primary importance to manage such visual information, by providing adequate data structures, expressing queries about them effectively, providing indexing facilities to access the databases and filter out images that are not related to the query content, and finally visualizing query results.

Multi-perspective Navigation of Movies

Digital libraries of video are rapidly growing in size and availability through digital networks of computers. The explosion of digital unstructure information, such as video, traveling through the network raises the need for tools that allow video information structuring and “intelligent” access to sequences. In this paper we present a novel video database system, which has been expressly designed to support structured storage of movies. A semiotic formalization for movie logical organization is here introduced and exploited to derive the conceptual schema of an object-oriented movie database. This structure is expressly tailored to enable queries on film technical features and on semantic contents as well, thus allowing feature fusion and access from multiple perspectives. A novel visual interaction scheme is implemented that allows users to formulate queries as filtering operations, that enable focussing on the interesting part of information, while skipping the rest. Formalization of the operators of “perspective” and “filtering” is thereby supplied. Retrieval of stored sequences by iconic motion example is also implemented for accessing sequences by contents. Examples are supplied of interaction with the database and of expressive power of visual query language.