PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Correlation Approach for Automatic Image Annotation
David Hardoon, Craig Saunders, Sandor Szedmak and John Shawe-Taylor
In: The 2'nd International Conference on Advanced Data Mining and Applications, 14-16 Aug 2006, Xi'An, China.

Abstract

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 ob jects 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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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:2285
Deposited By:David Hardoon
Deposited On:26 October 2006