国防科技大学学报Issue(6)：152-157,6.DOI:10.11887/j.cn.201406027
结合非负张量表示与扩展隐 Dirichlet 分配模型的图像标注
Extended latent Dirichlet allocation for image annotation of nonnegative tensor representation
摘要
Abstract
Automatic image annotation is a challenge task due to the well-known semantic gap.Considering the difference between low-level visual features and high-level semantic concepts,the framework of automatic image annotation from the two aspects,image representation and semantic modeling,was constructed.For image representation,a new method of regularized nonnegative tensor representation (RNTP)was presented to abstract the detailed high-order tensor structures according to human’s intuitive recognition.A three-level hierarchical Bayesian model,extended latent Dirichlet allocation (ELDA),was developed for semantic modeling.In ELDA,each item of multiple image factors was modeled as a finite mixture over latent variables.Meanwhile,an efficient expectation-maximization algorithm based on variational inference was proposed for parameter estimation.Extensive experimental results are reported on the NUS-WIDE dataset to validate the effectiveness of our proposed solution to the automatic image annotation problem by comparing with other state-of-the-art methods.关键词
图像标注/非负张量表示/扩展隐 Dirichlet分配/变分推理Key words
image annotation/nonnegative tensor representation/extended latent Dirichlet allocation/variational inference分类
信息技术与安全科学引用本文复制引用
钱智明,钟平,王润生..结合非负张量表示与扩展隐 Dirichlet 分配模型的图像标注[J].国防科技大学学报,2014,(6):152-157,6.基金项目
国家自然科学基金资助项目 （）
