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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 At CIVR '08: The 2008 international conference on Content-based image and video retrieval, Canada. 07 - 09 Jul 2008. , pp. 359-368.

Record type: Conference or Workshop Item (Poster)

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.

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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, 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

Catalogue record

Date deposited: 19 Jul 2008 10:20
Last modified: 18 Jul 2017 07:19

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Contributors

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

University divisions

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