Image annotation based on multi-view robust spectral clustering

•We propose a new multi-view robust image annotation model.•A new feature-level fusion method is proposed to learn the fusion similarity matrix.•A robust similarity measure based on the Maximum Correntropy Criterion is proposed.•Stability analysis and computational complexity of the proposed model are considered.•Geotags of Flickr and 500PX images are employed to improve the quality of the model. Nowadays, image annotation has been a hot topic in the semantic retrieval field due to the abundant growth of digital images. The purpose of these methods is to realize the content of images and assign appropriate keywords to them. Extensive efforts have been conducted in this field, which effectiveness is limited between low-level image features and high-level semantic concepts. In this paper, we propose a Multi-View Robust Spectral Clustering (MVRSC) method, which tries to model the relationship between semantic and multi-features of training images based on the Maximum Correntropy Criterion. A Half-Quadratic optimization framework is used to solve the objective function. According to the constructed model, a few tags are suggested based on a novel decision-level fusion distance. The stability condition and bound calculation of MVRSC are analyzed, as well. Experimental results on real-world Flickr and 500PX datasets, and Corel5K confirm the superiority of the proposed method over other competing models.