Simultaneous image classification and annotation based on probabilistic model |
| |
Authors: | LI Xiao-xu SUN Chao-bo LU Peng WANG Xiao-jie ZHONG Yi-xin |
| |
Affiliation: | Center for Intelligence Science and Technology, Beijing University of Posts and Telecommunications, Beijing 1000876, China |
| |
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. |
| |
Keywords: | image classification image annotation probabilistic model variational inference |
本文献已被 CNKI 维普 万方数据 ScienceDirect 等数据库收录! |
|