Abstract

In this paper, we present a novel method for image annota- tion and made three contributions. Firstly, we propose to use the tags contained in the training images as the supervising information to guide the generation of random trees, thus enabling the retrieved nearest neigh- bor images not only visually alike but also semantically related. Secondly, different from conventional decision tree methods, which fuse the infor- mation contained at each leaf node individually, our method treats the random forest as a whole, and introduces the new concepts of seman- tic nearest neighbors (SNN) and semantic similarity measure (SSM). Thirdly, we annotate an image from the tags of its SNN based on SSM and have developed a novel learning to rank algorithm to systematically assign the optimal tags to the image. The new technique is intrinsically scalable and we will present experimental results to demonstrate that it is competitive to state of the art methods.