Online Vicept learning for web-scale image understanding

Web-scale image understanding is a challenging but significant task to comprehend image contents on the internet. The de-facto standard methods based on machine learning or computer vision still suffer from a phenomenon of visual pol-ysemia and concept polymorphism (VPCP). To resolve the VPCP, Vicept has been proposed to characterize the membership distribution between visual appearances and semantic concepts. In this paper, we propose an online Vicept learning algorithm on the base of stochastic approximations, which can scale up to large scale datasets with millions of training samples. With the help of the Vicept, we develop an extension of the spatial pyramid matching (SPM) kernel method by generalizing the Vicept as a basic semantic description. The efficiency of our approach is validated in the experiments of web-scale semantic image search and image classification on the ImageNet dataset and Caltech-256 dataset.