关键词: 图像标注, 图像类别, 变分EM, Corr-LDA模型

Abstract: 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 provide 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 probabilistic 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.

Key words: image annotation, image class, variational expectation maximization, Corr-LDA model