Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud
With the rapid development of the cloud computing and mobile service, users expect a better experience through multimedia computing, such as automatic or semi-automatic personal image and video organization and intelligent user interface. These functions heavily depend on the success of image understanding, and thus large-scale image annotation has received intensive attention in recent years. The collaboration between mobile and cloud opens a new avenue for image annotation, because the heavy computation can be transferred to the cloud for immediately responding user actions. In this paper, we present a scheme for image annotation on the cloud, which transmits mobile images compressed by Hamming compressed sensing to the cloud and conducts semantic annotation through a novel Hessian regularized support vector machine on the cloud. We carefully explained the rationality of Hessian regularization for encoding the local geometry of the compact support of the marginal distribution and proved that Hessian regularized support vector machine in the reproducing kernel Hilbert space is equivalent to conduct Hessian regularized support vector machine in the space spanned by the principal components of the kernel principal component analysis. We conducted experiments on the PASCAL VOC'07 dataset and demonstrated the effectiveness of Hessian regularized support vector machine for large-scale image annotation.
cloud computing mobile service hessian eigenmaps and support vector machines manifold regularization