Non-Linear Matrix Completion for Social Image Tagging

In this paper, we address the problem of social image tagging using practical vocabulary for mobile users on the social media. On the social media, images usually have an incomplete or noisy set of social tags provided by the mobile users, and we consider this issue as defective tag assignments. Previous studies on social image tagging have mostly focused on multi-label classification without considering the defective tags. In these studies, the usage of multi-label classification techniques is expected to synergically exploit the linear relations between the image features and the semantic tags. However, these approaches usually aimed to capture the linear relations from the training data while ignoring the helpful information from the test data. In addition, they failed to incorporate the non-linear associations residing in the visual features as well as in the semantic tags. To overcome these drawbacks, we introduce a novel approach based on non-linear matrix completion for image tagging task with defective tags. Specifically, we first construct the entire feature-tag matrix based on the visual features with non-linear kernel mapping. Then, we present a formal methodology together with an optimization method under the matrix completion framework to jointly complete the tags of training and test images. Experimental evaluations demonstrate that our method shows promising results on image tagging task on two benchmark social image datasets with defective tags, and establishes a baseline for such models in this research domain.