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

Hare, J., Samangooei, S., Lewis, P. and Nixon, M. (2008) Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces. In: CIVR '08: The 2008 international conference on Content-based image and video retrieval, July 7-9 2008, Niagara Falls, Ontario, Canada. pp. 359-368.

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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.

Item Type:Conference or Workshop Item
Creator/Authors:
Jonathan Hare
Sina Samangooei
Paul Lewis
Mark Nixon
Keywords:semantic image retrieval, latent semantic analysis, LSA, LSI, PLSA, probabilistic latent semantic analysis, performance, auto-annotation
Research Group:Current ECS Groups > Communications, Signal Processing and Control
Old ECS Groups > Information - Signals, Images, Systems
Current ECS Groups > Web and Internet Science
Old ECS Groups > Intelligence, Agents, Multimedia
Old ECS Groups > Learning Societies Lab
Alternative Locations:http://doi.acm.org/10.1145/1386352.1386399
ISBN:978-1-60558-070-8
Date:7 July 2008
Information about this record:
Performance Indicator:EZ~04~04~04
Citations:Google Scholar: 14
Downloads (2010):97
ID Code:16160
Last Modified:23 Sep 2011 10:36
Deposited On:19 Jul 2008 10:20 by Hare, Jonathan

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References in Article

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