Collaborative and Content-based Image Labeling

Many on-line photo sharing systems allow users to tag their images so as to support semantic image search. In this paper we study how one can take advantages of the already tagged images to (semi-)automate the labeling of newly uploaded ones. In particular we propose a hybrid approach for the prediction where user-provided tags and image visual contents are,fused under a unified probabilistic framework. Kernel smoothing and collaborative filtering techniques are explored for improving the accuracy of the probabilistic models estimation. By comparing with some state-of-the-art content-based image labeling methods, we have empirically shown that 1) the proposed method can achieve comparable tag prediction accuracy when. there is no user-provided tag, and that 2) it can. significantly boost the prediction accuracy if the user can provide just a few tags.