Renmin University of China at ImageCLEF 2013 Scalable Concept Image Annotation
Xirong Li, Shuai Liao, Binbin Liu, Gang Yang, Qin Jin, Jieping Xu, Xiaoyong Du · CLEF (Working Notes) · 2013
In this paper we describe our image annotation system par- ticipated in the ImageCLEF 2014 scalable concept image annotation task. The system is fully SVM based. Per concept we learn an ensemble of fast intersection kernel SVMs from three sources of training data, all obtained with manual annotation for free. The focus of our experiments this year is to answer the question of how many tags we should use to annotate a novel image. To that end, we introduce adaptive tag selection. In contrast to the common top-k strategy which selects a fixed number of top ranked tags to annotate an unlabeled image, our method estimates the value of k with respect to the image. Given the same concept rank- ings, the top-5 strategy obtains MF-sample of 0.206, while adaptive tag selection reaches MF-sample of 0.311.