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Generic object recognition by combining distinct features in machine learning

Generic object recognition by combining distinct features in machine learning
Generic object recognition by combining distinct features in machine learning
In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. And these features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches from the same original images were studied by Kernel Canonical Correlation Analysis (KCCA). We apply a Support Vector Machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiment show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data.
KCCA, SVM, Data Fusion, Image Recognition, Feature Selection
90-98
Meng, Hongying
286b6735-6945-4dcf-abcf-81ae2aaf8255
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Meng, Hongying
286b6735-6945-4dcf-abcf-81ae2aaf8255
Hardoon, David R.
05549e24-da95-4690-a3e2-3c672d2342b8
Szedmak, Sandor
c6a84aa3-2956-4acf-8293-a1b676f6d7d8
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b

Meng, Hongying, Hardoon, David R., Szedmak, Sandor and Shawe-Taylor, John (2005) Generic object recognition by combining distinct features in machine learning. 17th Annual Symposium on Electronic Imaging, San Jose, California, United States. pp. 90-98 .

Record type: Conference or Workshop Item (Other)

Abstract

In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. And these features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches from the same original images were studied by Kernel Canonical Correlation Analysis (KCCA). We apply a Support Vector Machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiment show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data.

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More information

Published date: 2005
Additional Information: Event Dates: 16-20 January 2005
Venue - Dates: 17th Annual Symposium on Electronic Imaging, San Jose, California, United States, 2005-01-01
Keywords: KCCA, SVM, Data Fusion, Image Recognition, Feature Selection
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 260656
URI: http://eprints.soton.ac.uk/id/eprint/260656
PURE UUID: a638de9e-ca05-4159-bad2-ce6b6b08f018

Catalogue record

Date deposited: 08 Mar 2005
Last modified: 14 Mar 2024 06:41

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Contributors

Author: Hongying Meng
Author: David R. Hardoon
Author: Sandor Szedmak
Author: John Shawe-Taylor

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