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Non-negative Matrix Factorisation for Object Class Discovery and Image Auto-annotation

Non-negative Matrix Factorisation for Object Class Discovery and Image Auto-annotation
Non-negative Matrix Factorisation for Object Class Discovery and Image Auto-annotation
In information retrieval, sub-space techniques are usually used to reveal the latent semantic structure of a data-set by projecting it to a low dimensional space. Non-negative matrix factorisation (NMF), which generates a non-negative representation of data through matrix decomposition, is one such technique. It is different from other similar techniques, such as singular vector decomposition (SVD), in its non-negativity constraints which lead to its parts-based representation characteristic. In this paper, we present the novel use of NMF in two tasks; object class detection and automatic annotation of images. Experimental results imply that NMF is a promising sub-space technique for discovering the latent structure of image data-sets, with the ability of encoding the latent topics that correspond to object classes in the basis vectors generated.
Tang, Jiayu
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Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020
Tang, Jiayu
4f9409ac-830d-4937-867d-e06c76b8a4e1
Lewis, Paul
7aa6c6d9-bc69-4e19-b2ac-a6e20558c020

Tang, Jiayu and Lewis, Paul (2008) Non-negative Matrix Factorisation for Object Class Discovery and Image Auto-annotation. ACM International Conference on Image and Video Retrieval, Niagara Falls, Canada. 06 - 08 Jul 2008.

Record type: Conference or Workshop Item (Paper)

Abstract

In information retrieval, sub-space techniques are usually used to reveal the latent semantic structure of a data-set by projecting it to a low dimensional space. Non-negative matrix factorisation (NMF), which generates a non-negative representation of data through matrix decomposition, is one such technique. It is different from other similar techniques, such as singular vector decomposition (SVD), in its non-negativity constraints which lead to its parts-based representation characteristic. In this paper, we present the novel use of NMF in two tasks; object class detection and automatic annotation of images. Experimental results imply that NMF is a promising sub-space technique for discovering the latent structure of image data-sets, with the ability of encoding the latent topics that correspond to object classes in the basis vectors generated.

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More information

Published date: 18 April 2008
Additional Information: Event Dates: July 7-9, 2008
Venue - Dates: ACM International Conference on Image and Video Retrieval, Niagara Falls, Canada, 2008-07-06 - 2008-07-08
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 265452
URI: http://eprints.soton.ac.uk/id/eprint/265452
PURE UUID: 311d1266-d708-4bf4-a9ed-595f9f7b294a

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Date deposited: 18 Apr 2008 15:35
Last modified: 20 Nov 2021 08:11

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Contributors

Author: Jiayu Tang
Author: Paul Lewis

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