Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation

Authors:
Caijuan Shi;Qiuqi Ruan;Gaoyun An
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and College of Information Engineering, Hebei United University, Tangshan 063009, China and Beijing Key Laborat ...;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China
Venue:
Image and Vision Computing
Year:
2014

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Abstract

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). 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.