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一种基于本体与描述文本的网络图像语义标注方法 被引量：4

Semantic Annotation Method for Web Image Based on Domain Ontology and Image Description Texts
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摘要 网络图像语义自动标注是实现对互联网中海量图像管理和检索的有效途径,而自动有效地挖掘图像语义是实现自动语义标注的关键。网络图像的语义蕴含于图像自身,但更多的在于对图像语义起不同作用的各种描述文本,而且随着图像和描述知识的变化,描述文本所描述的图像语义也随之变化。提出了一种基于领域本体和不同描述文本语义权重的自适应学习的语义自动标注方法,该方法从图像的文本特征出发考查它们对图像语义的影响,先通过本体进行有效的语义快速发现与语义扩展,再利用一种加权回归模型对图像语义在其不同类型描述文本上的分布进行自适应的建模,进而实现对网络图像的语义标注。在真实的Web数据环境中进行的实验中,该方法的有效性得到了验证。 Semantic auto annotation on Web image is an important method for huge amounts of images management and retrieval on Web,and the semantic mining from the images automatically and effectively is the key.The semantic lies not only in the image itself,but also and more importantly in its description texts.Further,the image semantic varies with the change of images or description knowledge.To address this issue,in this paper,based on domain ontology and different image descriptions,we propose an adaptive semantic annotation method for Web images.This method checks the impacts on image semantic from the description texts feature.It detects the semantic and extends keyword by domain ontology,and then based on a regression model to adaptively model the Web images’ semantic distribution on its different description texts.A group of experiments are carried out on a real-world Web image data set and the experimental results show that our proposed method outperforms others is with excellent adaptivity.
作者 陈叶旺 钟必能 王靖 李海波
机构地区 华侨大学计算机科学学院
出处 《计算机科学》 CSCD 北大核心 2012年第B06期293-299,共7页 Computer Science
基金 国家自然科学基金(10901062) 福建农业科技重大项目(2010N5008)资助
关键词 图像标注 本体 语义扩展 回归模型 Image annotation； Ontology； Semantic extension； Regression model
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献30

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二级参考文献58

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共引文献23

同被引文献25

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引证文献4

二级引证文献13

计算机科学
2012年 第B06期

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