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ADMA
2006
Springer

A Correlation Approach for Automatic Image Annotation

10 years 2 months ago
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.
David R. Hardoon, Craig Saunders, Sándor Sz
Added 13 Jun 2010
Updated 13 Jun 2010
Type Conference
Year 2006
Where ADMA
Authors David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
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