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Learning in Region-Based Image Retrieval with Generalized Support Vector Machines

9 years 28 days ago
Learning in Region-Based Image Retrieval with Generalized Support Vector Machines
Relevance feedback approaches based on support vector machine (SVM) learning have been applied to significantly improve retrieval performance in content-based image retrieval (CBIR). Those approaches require the use of fixedlength image representations because SVM kernels represent an inner product in a feature space that is a non-linear transformation of the input space. Many region-based CBIR approaches create a variable length image representation and define a similarity measure between two variable length representations. The standard SVM approach cannot be applied to this approach because it violates the requirements that SVM places on the kernel. Fortunately, a generalized SVM (GSVM) has been developed that allows the use of an arbitrary kernel. In this paper, we present an initial investigation into utilizing a GSVM-based relevance feedback learning algorithm. Since GSVM does not place restrictions on the kernel, any image similarity measure can be used. In particular, the prop...
Iker Gondra, Douglas R. Heisterkamp
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CVPR
Authors Iker Gondra, Douglas R. Heisterkamp
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