Content-based retrieval applications on a common database management system

Kosugi, Naoko; Nishimura, Go; Teramoto, Junji; Mii, Kazuyoshi; Onizuka, Makoto; Kon'ya, Seiichi; Kojima, Akira; Kataoka, Ryuho; Honishi, Takashi; Kushima, Kazuhiko

doi:10.1145/500141.500254

Cited by 3 publications

(1 citation statement)

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“…In relation to content definition, search and archiving, [15] and [25] present some team-based mechanisms for simplifying these operations on digital content. Furthermore, [14] supports image-based searches, whereas [1] and [21] analyze metadata requirements and organization for multimedia content in various domains. [4] discusses automatic video annotation and retrieval using ontologies.…”

Section: Researchmentioning

confidence: 99%

Digital art 2.0

Christodoulou

Styliaras

2008

Proceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts

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In this paper, we describe a review of existing Web 2.0 applications and the current or potential usability of them during the lifecycle of digital art projects. More specifically, we outline the characteristics of the various digital art categories, and the features that need to be supported. Afterwards, we present a classification of Web 2.0 applications and we show how they can support the digital art projects in all categories and lifecycle steps, esp. regarding collaboration, as well as some future potentials.

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Section: Researchmentioning

confidence: 99%

Digital art 2.0

Christodoulou

Styliaras

2008

Proceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts

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A human-based technique for measuring video data similarity

Farag

Abdel-Wahab

Proceedings of the Eighth IEEE Symposium on Computers and Communications. ISCC 2003

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Video Content-Based Retrieval Techniques

Farag

Multimedia Systems and Content-Based Image Retrieval

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The increasing use of multimedia streams nowadays necessitates the development of efficient and effective methodologies and systems for manipulating databases storing these streams. These systems have various areas of application such as video-on-demand and digital libraries. The importance of video content-based retrieval (CBR) systems motivates us to explain their basic components in this chapter and shed light on their underlying working principles. In general, a content-based retrieval system of video data consists of the following four stages: (1) Video Shot Boundary Detection, (2) Key Frames (KFs) selection, (3) features extraction (from selected KFs), and (4) retrieval stage (where similarity matching operations are performed). Each one of the above stages will be reviewed and expounded based on our experience in building a Video Content-based Retrieval (VCR) system that has been fully implemented from scratch in JAVA Language (2002). Moreover, current research directions and outstanding problems will be discussed for each stage in the context of our VCR system.

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Content-based retrieval applications on a common database management system

Cited by 3 publications

References 7 publications

Digital art 2.0

Digital art 2.0

A human-based technique for measuring video data similarity

Video Content-Based Retrieval Techniques

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