A renewed image annotation baseline by image embedding and tag correlation

This paper presents a renewed image annotation baseline method under the nearest neighbor tag transfer framework. Two key problems are considered in this paper: (1) which images are determined as the neighbors; (2) how their keywords are transferred. Firstly, a soft neighbor selection scheme is designed by image embedding technique, with which we can provide more power to the crucial neighbors in decision making. Next, diffused tag propagation is introduced to allow one tag be propagated to other relevant tags. Besides this, the above two measures are formulated into an optimization framework to further improve the prediction performance. Experimental results on standard database show that the proposed approaches outperform the current state-of-the-art methods.