Image classification by combining multiple SVMS

In this paper, a novel framework is proposed for classifying images, which integrates several sets of Support Vector Machines(SVM) on multiple low level image features. In the proposed framework several global image features are extracted from the input images, and SVM using linear kernel with probability outputs are constructed on each feature. The outputs of the SVM classifiers are then combined by g lambda-fuzzy integral. The density value of the fuzzy integral for each classifier is trained by using grid searching algorithm. Compared with sonic current systems, our proposed framework demonstrates a promising performance for an image database of general-purpose images from Corel image library.