The University of Southampton
University of Southampton Institutional Repository

Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces

Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces
Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces
Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval.
semantic image retrieval, latent semantic analysis, LSA, LSI, PLSA, probabilistic latent semantic analysis, performance, auto-annotation
978-1-60558-070-8
359-368
Hare, Jonathan
65ba2cda-eaaf-4767-a325-cd845504e5a9
Samangooei, Sina
c380fb26-55d4-4b34-94e7-c92bbb26a40d
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Hare, Jonathan
65ba2cda-eaaf-4767-a325-cd845504e5a9
Samangooei, Sina
c380fb26-55d4-4b34-94e7-c92bbb26a40d
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Hare, Jonathan, Samangooei, Sina, Lewis, Paul and Nixon, Mark (2008) Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces. CIVR '08: The 2008 international conference on Content-based image and video retrieval, Niagara Falls, Ontario, Canada. 07 - 09 Jul 2008. pp. 359-368 .

Record type: Conference or Workshop Item (Paper)

Abstract

Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval.

Text
p359.pdf - Version of Record
Download (320kB)

More information

Published date: 7 July 2008
Additional Information: Event Dates: July 7-9 2008
Venue - Dates: CIVR '08: The 2008 international conference on Content-based image and video retrieval, Niagara Falls, Ontario, Canada, 2008-07-07 - 2008-07-09
Keywords: semantic image retrieval, latent semantic analysis, LSA, LSI, PLSA, probabilistic latent semantic analysis, performance, auto-annotation
Organisations: Vision, Learning and Control, Web & Internet Science

Identifiers

Local EPrints ID: 266160
URI: http://eprints.soton.ac.uk/id/eprint/266160
ISBN: 978-1-60558-070-8
PURE UUID: 4eba3bcd-2537-45eb-9d32-ad823b7ffe06
ORCID for Jonathan Hare: ORCID iD orcid.org/0000-0003-2921-4283
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 19 Jul 2008 10:20
Last modified: 15 Mar 2024 03:25

Export record

Contributors

Author: Jonathan Hare ORCID iD
Author: Sina Samangooei
Author: Paul Lewis
Author: Mark Nixon ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×