中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (2): 107-115.doi: : 10.1016/S1005-8885(11)60254-9

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Simultaneous image classification and annotation based on probabilistic model

李晓旭,孙超博,鲁鹏,王小捷,ZHONG Yi-xin   

  1. 北京邮电大学
  • 收稿日期:2011-07-14 修回日期:2011-12-26 出版日期:2012-04-30 发布日期:2012-04-17
  • 通讯作者: 李晓旭 E-mail:xiaoxulibupt@gmail.com
  • 基金资助:

    This work was supported by the Major Research Plan of the National Natural Science Foundation of China (90920006).

Simultaneous image classification and annotation based on probabilistic model

  1. Center for Intelligence Science and Technology, Beijing University of Posts and Telecommunications, Beijing 1000876, China
  • Received:2011-07-14 Revised:2011-12-26 Online:2012-04-30 Published:2012-04-17
  • Contact: xiao xuli E-mail:xiaoxulibupt@gmail.com
  • Supported by:

    This work was supported by the Major Research Plan of the National Natural Science Foundation of China (90920006).

摘要:

The paper proposes a novel probabilistic generative model for simultaneous image classification and annotation. The model considers the fact that the category information can provide valuable information for image annotation. Once the category of an image is ascertained, the scope of annotation words can be narrowed, and the probability of generating irrelevant annotation words can be reduced. To this end, the idea that annotates images according to class is introduced in the model. Using variational methods, the approximate inference and parameters estimation algorithms of the model are derived, and efficient approximations for classifying and annotating new images are also given. The power of our model is demonstrated on two real world datasets: a 1 600-images LabelMe dataset and a 1 791-images UIUC-Sport dataset. The experiment results show that the classification performance is on par with several state-of-the-art classification models, while the annotation performance is better than that of several state-of-the-art annotation models.

关键词:

image classification, image annotation, probabilistic model, variational inference

Abstract:

The paper proposes a novel probabilistic generative model for simultaneous image classification and annotation. The model considers the fact that the category information can provide valuable information for image annotation. Once the category of an image is ascertained, the scope of annotation words can be narrowed, and the probability of generating irrelevant annotation words can be reduced. To this end, the idea that annotates images according to class is introduced in the model. Using variational methods, the approximate inference and parameters estimation algorithms of the model are derived, and efficient approximations for classifying and annotating new images are also given. The power of our model is demonstrated on two real world datasets: a 1 600-images LabelMe dataset and a 1 791-images UIUC-Sport dataset. The experiment results show that the classification performance is on par with several state-of-the-art classification models, while the annotation performance is better than that of several state-of-the-art annotation models.

Key words:

image classification, image annotation, probabilistic model, variational inference