Abstract

Image annotation as well as classifi cation are both critical and chal- lenging work in computer vision research. Due to the rapid increasing number of images and inevitable biased annotation or classi fication by the human curator, it is desired to have an automatic way. Recently, there are lots of methods proposed regarding image classi fication or image annotation. However, people usually treat the above two tasks independently and tackle them separately. Actually, there is a relationship between the image class label and image annotation terms. As we know, an image with the sport class label rowing is more likely to be annotated with the terms water, boat and oar than the terms wall, net and floor, which are the descriptions of indoor sports. In this paper, we propose a new method for jointly class recognition and terms annotation. We present a novel Tri-Relational Graph (TG) model that comprises the data graph, annotation terms graph, class label graph, and connect them by two additional graphs induced from class label as well as annotation assignments. Upon the TG model, we introduce a Biased Random Walk (BRW) method to jointly recognize class and annotate terms by utilizing the interrelations between two tasks. We conduct the proposed method on two benchmark data sets and the experimental results demonstrate our joint learning method can achieve superior prediction results on both tasks than the state-of-the-art methods.