Training support vector machines based on stacked generalization for image classification

This paper presents a two-level stacked generalization scheme composed of three generalizers of support vector machines (SVMs) for image classification. They are color, texture, and high-level concept SVMs. The focus of this paper is to investigate two training strategies based on two-fold cross-validation and non-cross-validation for the proposed classification scheme by evaluating their classification performances, margin of the hyperplane and numbers of support vectors of SVMs. The results show that the non-cross-validation training method performs better, having higher correct classification rates, larger margin of the hyperplane, and smaller numbers of support vectors.