Automatic image annotation by semi-supervised manifold kernel density estimation

作者：Ji, Ping^*; Zhao, Na; Hao, Shijie; Jiang, Jianguo

来源：Information Sciences, 2014, 281: 648-660.

DOI：10.1016/j.ins.2013.09.016

• Image annotation
• Semi-supervised learning
• Kernel density estimation
• Manifold

The insufficiency of labeled training data is a major obstacle in automatic image annotation. To tackle this problem, we propose a semi-supervised manifold kernel density estimation (SSMKDE) approach based on a recently proposed manifold KDE method. Our contributions are twofold. First, SSMKDE leverages both labeled and unlabeled samples and formulates all data in a manifold structure, which enables a more accurate label prediction. Second, the relationship between KDE-based methods and graph-based semi-supervised learning (SSL) methods is analyzed, which helps to better understand graph-based SSL methods. Extensive experiments demonstrate the superiority of SSMKDE over existing KDE-based and graph-based SSL methods.

• 出版日期2014-10-10
• 单位合肥学院; 合肥工业大学