A Correlation Approach for Automatic Image Annotation
A Correlation Approach for Automatic Image Annotation
The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.
KCCA, Correlation Analysis
978-3-540-37025-3
681-692
Hardoon, D.
eff48e83-f1d8-4b48-b57e-9bf21ae91679
Saunders, C.
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Szedmak, S.
993ca93f-c7c7-4d0b-a5f7-374eb0401add
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Li, Xue
392b9143-c1da-416a-82d8-5b6e7880a2bb
Zaiane, Osmar
2560714a-32c5-45d1-817a-1f588b84fa38
Li, Zahnhuai
f3faf621-d134-4b2e-a598-ae5298058ab0
2006
Hardoon, D.
eff48e83-f1d8-4b48-b57e-9bf21ae91679
Saunders, C.
38a38da8-1eb3-47a8-80bc-b9cbb43f26e3
Szedmak, S.
993ca93f-c7c7-4d0b-a5f7-374eb0401add
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Li, Xue
392b9143-c1da-416a-82d8-5b6e7880a2bb
Zaiane, Osmar
2560714a-32c5-45d1-817a-1f588b84fa38
Li, Zahnhuai
f3faf621-d134-4b2e-a598-ae5298058ab0
Hardoon, D., Saunders, C., Szedmak, S. and Shawe-Taylor, J.
(2006)
A Correlation Approach for Automatic Image Annotation.
Li, Xue, Zaiane, Osmar and Li, Zahnhuai
(eds.)
Second International Conference on Advanced Data Mining and Applications, ADMA 2006, Xi'an, China.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.
More information
Published date: 2006
Additional Information:
Event Dates: August
Venue - Dates:
Second International Conference on Advanced Data Mining and Applications, ADMA 2006, Xi'an, China, 2006-08-01
Keywords:
KCCA, Correlation Analysis
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 264035
URI: http://eprints.soton.ac.uk/id/eprint/264035
ISBN: 978-3-540-37025-3
PURE UUID: 78e5f7bd-43ca-40ce-82c3-f96906de65bc
Catalogue record
Date deposited: 21 May 2007
Last modified: 14 Mar 2024 07:41
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Contributors
Author:
D. Hardoon
Author:
C. Saunders
Author:
S. Szedmak
Author:
J. Shawe-Taylor
Editor:
Xue Li
Editor:
Osmar Zaiane
Editor:
Zahnhuai Li
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