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...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Iker Gondra, Douglas R. Heisterkamp
• Keywords: Computer Vision, CVPR 2004, Image Similarity Measure, Similarity Measure, Variable Length Representations