A rank minimization-based late fusion method for multi-label image annotation
Yao Yao, Xin Xin, Ping Guo · 2016
Image annotation is a hard multi-label learning problem which aims at automatically tagging each input image with relevant keywords reflecting its semantic concepts. Recently, several late fusion methods were proposed to improve the accuracy of image annotation. But these late fusion methods need normalization of confidence score vectors of independent models corresponding to distinct representations. Choosing a good normalization function is tricky and difficult. In this paper, we propose a new method of late fusion for image annotation based on rank minimization. The proposed method avoids normalization by transforming confidence score vectors into pairwise relationship matrices. And an optimal matrix is obtained by solving a minimization optimization problem. With the optimal matrix, a fused confidence score vector can be recovered, which gives the final prediction of tags. Experiments on standard Corel5K and our Campus-Indoor dataset confirm the effectiveness of our late fusion method for image annotation.