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Monday, August 3, 2020 | History

3 edition of Storage and retrieval for image and video databases II found in the catalog.

Storage and retrieval for image and video databases II

7-8 February 1994, San Jose, California

  • 189 Want to read
  • 40 Currently reading

Published by SPIE in Bellingham, Wash., USA .
Written in English

    Subjects:
  • Optical storage devices -- Congresses.,
  • Image processing -- Congresses.,
  • Image processing -- Digital techniques -- Congresses.,
  • Database management -- Congresses.

  • Edition Notes

    Include bibliographical references and index.

    Other titlesStorage and retrieval for image and video databases 2., Storate and retrieval for image and video databases two.
    StatementWayne Niblack, chair/editor ; sponsored by IS&T--the Society for Imaging Science and Technology, SPIE--the International Society for Optical Engineering.
    SeriesProceedings / SPIE--the International Society for Optical Engineering -- v. 2185, Proceedings of SPIE--the International Society for Optical Engineering -- v. 2185.
    ContributionsNiblack, Wayne., IS & T--the Society for Imaging Science and Technology., Society of Photo-optical Instrumentation Engineers.
    Classifications
    LC ClassificationsTA1635 .S855 1993
    The Physical Object
    Paginationiv, 250 p. :
    Number of Pages250
    ID Numbers
    Open LibraryOL19827909M
    ISBN 100819414808

    Performance Evaluation of Storage and Retrieval of DICOM Image Content in Oracle Database 11g Using HP Blade Servers and Intel Processors 1 Overview Several of the world’s leading PACS vendors asked us to meet their image retrieval requirement of Cardiac CT images per second with DICOM images stored in Oracle Database.   Storage and retrieval for image and video databases 3 Storage and retrieval for image and video databases three: Responsibility: Wayne Niblack, Ramesh C. Jain, chairs/editors ; sponsored by IS & T--the Society for Imaging Science and Technology, SPIE--The International Society for Optical Engineering.

    just storehouses of books, but also places where the information they hold is the specific nature of audio, image and video information has called for the development of many new tools and techniques for information 1 Basic concepts of information retrieval systems. retrieval. Modern information retrieval deals with storage, organization. Pentland A et al "Photobook: tools for content-based manipulation of image databases" in Storage and Retrieval for Image and Video Databases II (Niblack W R & Jain R C, eds), Proc SPIE , pp (). Google Scholar; Eakins J P "Design and evaluation of a shape retrieval system" PhD thesis, University of Newcastle upon Tyne (

    Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within hypertext collections such as the Internet or intranets. IR is further analyzed to text retrieval, document retrieval, and image, video, or sound is an interdisciplinary scientific field based.   Multimedia database is the collection of interrelated multimedia data that includes text, graphics (sketches, drawings), images, animations, video, audio etc and have vast amounts of multisource multimedia data. The framework that manages different types of multimedia data which can be stored, delivered and utilized in different ways is known as multimedia database management .


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Storage and retrieval for image and video databases II Download PDF EPUB FB2

Get this from a library. Storage and retrieval for image and video databases II. [Wayne Niblack; Ramesh Jain; IS & T--the Society for Imaging Science and Technology.; Society of Photo-optical Instrumentation Engineers.; SPIE Digital Library.;].

Storage and retrieval for image and video databases II: FebruarySan Jose, California. [Wayne Niblack; Ramesh Jain; IS & T--the Society for Imaging Science and Technology.; Society of Photo-optical Instrumentation Engineers.;].

This book provides an in-depth treatment of the three important topics related to image and video databases: restoration, watermarking and retrieval. It is the result of the participation of the Delft University of Technology in the European Union ACTS program, a pre-competitive R&D program on Advanced Communications Technologies and Services ().Book Edition: 1.

COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus.

Storage and Retrieval for Image and Video Databases Editor(s): Carlton Wayne Niblack *This item is only available on the SPIE Digital Library. Get this from a library. Storage and Retrieval for Image and Video Databases Iii/Volume [Niblack].

Content Based Image Retrieval or CBIR is the retrieval of images based on visual features such as colour, texture and shape (Michael et al., ). Reasons for its development are that in many large image databases, traditional methods of image indexing have proven to be insufficient, laborious, and extremely time consuming.

Storage and Retrieval for Image and Video Databases VII Editor(s): Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman *This item is only available on the SPIE Digital Library. Storage and Retrieval for Image and Video Databases V Ishwar K.

Sethi Ramesh C. jain Chairs/Editors February San Jose, California Sponsored by IS&T—The Society for Imaging Science and Technology SPIE—The International Society for Optical Engineering P Volume   It's better to store images & videos in plain files rather than in a DB. This way you will minimize the CPU usage and the number of iops on uploading/downloading images and videos and playing videos.

I'm using xfs filesystem for that reason as it. Photobook. Photobook is a tool for performing queries on image databases based on image content. It works by comparing features associated with images, not the images themselves.

These features are in turn the parameter values of particular models fitted to each image. These models are commonly color, texture, and shape, though Photobook will work with features from any model. This volume presents the state of the art in digital image database design, with a concentration on storage and retrieval techniques, and includes a set of selected application case studies.

Chapters by experts from around the world explore a variety of techniques for accessing images based on color, texture, shape, and semantic s: 1. There are a huge number of research works focusing on the searching mechanisms in image databases for efficient retrieval and tried to give supplementary suggestions on the overall systems.

The growing of digital medias (digital camera, digital video, digital TV, e-book, cell phones, etc.) gave rise to the revolution of very large multimedia. Similarly, the CRR achieves a 98 percent reduction for an image database of 1GB and 20 objects per image.

There are over 45 references at the end of the paper. People who are interested in efficient storage and retrieval of images in very large image databases should read this paper. Scassellati, B.

et al, “Retrieving images by 2D shape: a comparison of computation methods with human perceptual judgements”, Storage and Retrieval for image and Video Databases II, 7–8 Feb.San Jose, California, USA, SPIE Proceedings Series, Vol. pp.2– Google Scholar. W. Niblack, J. Ramesh (Eds.), Proceedings of the Conference on Storage and Retrieval for Image and Video Databases II, San Jose, California, 7–8 FebruarySPIE Proceedings Series, Vol.

The combined feature vector is then used for indexing all salient regions of an image. For retrieval, those images are selected that contain more similar regions to the query image by using a multipass retrieval and ranking mechanism. Matches are found using the L 2 metric.

The results demonstrate that the proposed method performs very well. Books; SIGs; Conferences; People; More. A Comparison of Computation Methods with Human Perceptual Judgments," Storage and Retrieval for Image and Video Databases II (Proc. SPIE ), W.R. Niblack and R.C. Jain, eds "Similarity Indexing: Algorithms and Performance" Storage and Retrieval for Image and Video Databases IV (Proc.

SPIE Pentland, A.P., et al.: Photobook: tools for content-based manipulation of image databases. In: Storage and Retrieval for Image and Video Databases II, vol. pp. 34– International Society for Optics and Photonics () Google Scholar.

Inf. Syst. 3, (). Holt and L. Hartwick, Visual image retrieval for applications in art and art history in SPIE Vol. Storage and Retrieval for Image and Video Databases II, pp. 70 81 (February ).

Igarashi (Editor) World Trademarks and Logotypes. Graphic-sha, Tokyo ). Ahanger, G., Benson, D. and Little, T.D., “Video Query Formulation,” Proceedings of Storage and Retrieval for Images and Video Databases II, San Jose, February.

M. Stricker, Bounds for the Discrimination Power of Color Indexing Techniques, Storage and Retrieval for Image and Video Databases II of SPIE .ad-hoc commercial solutions for searching imagery, video, and other multimedia types.

There still is a lack, however, of large commercial systems that integrate existing techniques for storage, indexing, and retrieval of image databases. In this volume, we present .