计算机工程与应用Issue(10)：187-192,6.DOI:10.3778/j.issn.1002-8331.1512-0234
融入类别信息的图像标注概率主题模型
Image annotation probabilistic topic model fusing class information
摘要
Abstract
ion:The image annotation method based on the probabilistic topic model annotates images by learning the semantic of the image, and researchers pay more and more attention to it in recent years. Class label information can pro-vide the valuable information for image annotation, for example, for images in"tall building"class, annotating"sky","skyscraper"is more possible than annotating"sea"and"beach". However, for images in"coast"class, annotating"sea","beach"is more possible than annotating"sky"and"skyscraper". This paper proposes an image annotation proba-bilistic topic model fusing class information which uses class information to promote image annotation. And it derives a parameters estimation algorithm based on the variational EM algorithm, as well as gives the method annotating the new images. The experimental results on LableMe and UIUC-Sport datasets show that the image annotation performance of the proposed model is better than other contrastive models.关键词
图像标注/图像类别/变分EM/Corr-LDA模型Key words
image annotation/image class/variational expectation maximization/Corr-LDA model分类
信息技术与安全科学引用本文复制引用
曹洁,罗菊香,李晓旭..融入类别信息的图像标注概率主题模型[J].计算机工程与应用,2017,(10):187-192,6.基金项目
国家自然科学基金(No.61263031) （No.61263031）
甘肃省自然科学基金(No.1310RJZA034) （No.1310RJZA034）
