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. In, Second International Conference on Advanced Data Mining and Applications, ADMA 2006, Xi'an, China, Springer, 681-692.


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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: August
ISBNs: 9783540370253
Keywords: KCCA, Correlation Analysis
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 264035
Accepted Date and Publication Date:
Date Deposited: 21 May 2007
Last Modified: 31 Mar 2016 14:08
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/264035

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