Sparse feature selection based on graph Laplacian for web image annotation

作者：Shi, Caijuan^*; Ruan, Qiuqi; An, Gaoyun

来源：Image and Vision Computing, 2014, 32(3): 189-201.

DOI：10.1016/j.imavis.2013.12.013

• Web image annotation
• Sparse feature selection
• l(2,1/2)-matrix norm
• Semi-supervised learning
• Graph Laplacian

Confronted with the explosive growth of web images, the web image annotation has become a critical research issue for image search and index. Sparse feature selection plays an important role in improving the efficiency and performance of web image annotation. Meanwhile, it is beneficial to developing an effective mechanism to leverage the unlabeled training data for large-scale web image annotation. In this paper we propose a novel sparse feature selection framework for web image annotation, namely sparse Feature Selection based on Graph Laplacian (FSLG)(2). FSLG applies the l(2,1/2)-matrix norm into the sparse feature selection algorithm to select the most sparse and discriminative features. Additional, graph Laplacian based semi-supervised learning is used to exploit both labeled and unlabeled data for enhancing the annotation performance. An efficient iterative algorithm is designed to optimize the objective function. Extensive experiments on two web image datasets are performed and the results illustrate that our method is promising for large-scale web image annotation.

• 出版日期2014-3
• 单位华北理工大学; 北京交通大学