Semi-supervised Learning on Bi-relational Graph for Image Annotation

Hien Duy Pham, Kye-Hyeon Kim, Seungjin Choi

Abstract: We present a semi-supervised learning algorithm based on local and global consistency, working on a bi-relational graph of images and labels. By incorporating two types of different entities (images and labels) in a single graph, label propagation can exploit label correlations for measuring the relevance between unannotated images and labels, leading to a significant improvement in performance. In our propagation process, images belonging to the same label (or labels belonging to the same image) are not treated equally: our method allows that those images (or labels) have different weights in the label propagation process according to their semantic reliability to the label (or to the image), so that can achieve further improvement in the image annotation performance, compared to the existing work using a bi-relational graph. We apply our method to two benchmark multi-label image datasets, and obtain some encouraging experimental results.