Learning the semantics of multimedia content with application to web image retrieval and classification.
Presented at: Fourth International Symposium on Independent Component Analysis and Blind Source Separation, 2003.
We use kernel Canonical Correlation Analysis to learn a semantic representation of Web images and their associated text. This representation is used in two applications. In first application we consider classification of images into one of three categories. We use SVM in the semantic space and compare against the SVM on raw data and against previously published results using ICA. In the second application we retrieve images based only on their content from a text query. The semantic space provides a common representation and enables a comparison between the text and image. We compare against a standard cross-representation retrieval technique known as the Generalised Vector Space Model
|Type:||Conference item (UNSPECIFIED)|
|Title:||Learning the semantics of multimedia content with application to web image retrieval and classification|
|Event:||Fourth International Symposium on Independent Component Analysis and Blind Source Separation, 2003|
|Keywords:||Canonical correlation analysis, Correlation Analysis, KCCA, kernel canonical correlation analysis, SVM|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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