A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization. (27th June 2018)

• Record Type: Journal Article
• Authors: Ge, Hongwei; Yan, Zehang; Dou, Jing; Wang, Zhen; Wang, ZhiQiang
• Other Names: Zhang Qian Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-06-27
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/5987906
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.