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A Correlation Approach for Automatic Image Annotation

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.) At Second International Conference on Advanced Data Mining and Applications, ADMA 2006, China. , pp. 681-692.

Record type: Conference or Workshop Item (Paper)


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.

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Published date: 2006
Additional Information: Event Dates: August
Venue - Dates: Second International Conference on Advanced Data Mining and Applications, ADMA 2006, China, 2006-08-01
Keywords: KCCA, Correlation Analysis
Organisations: Electronics & Computer Science


Local EPrints ID: 264035
ISBN: 978-3-540-37025-3
PURE UUID: 78e5f7bd-43ca-40ce-82c3-f96906de65bc

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Date deposited: 21 May 2007
Last modified: 18 Jul 2017 07:40

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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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