VDBMS: A testbed facility for research in video database benchmarking

Abstract

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of videobased applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting lowlevel visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms

“…CIRES (content-based image retrieval system) is an image similarity search engine (Iqbal & Aggarwal, 2002); the system is able to serve queries ranging from scenes of purely natural objects such as vegetation, trees, and skies to images containing conspicuous structural objects such as buildings, towers, and bridges. VDBMS (Video Database Management System) is a data management system for advancing video database research (Aref et al 2002); it supports comprehensive and efficient database management for digital video databases, including feature-based pre-processing for video content representation and indexing, video and meta-data storage, video query processing, buffer and storage management, and continuous video streaming. Moreover, XQuery is used to generate dynamic queries and user-oriented summaries.…”

A multi-videos retrieval using adaptive key feature set of shot

“…We have characterized and extracted from the frame. General image analysis in the study of the [8][9][10], the most commonly used imaging features are color, texture, spatial relations and shapes or other features. However, too many videos with the lot of frame, the similar value between consecutive frames.…”

A survey of top- k query processing techniques in relational database systems

“…Example 1.2 draws attention to the importance of efficient top-k processing in similarity queries. In video databases [Aref et al 2004], hours of video data are stored inside the database producing huge amounts of data. Top-k similarity queries are traditionally answered using high-dimensional indexes built on individual video features, and a nearest-neighbor scan operator on top of these indexes.…”