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 fixed-length 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 proposed approach uses an image similarity measure developed for region-based, variable length representations. Experimental results over real world images demonstrate the efficacy of the proposed method.