基于Corr-LDA模型的图像标注方法

doi:10.13229/j.cnki.jdxbgxb20170350

吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (4): 1237-1243.doi: 10.13229/j.cnki.jdxbgxb20170350

基于Corr-LDA模型的图像标注方法

曹洁¹, 苏哲¹, 李晓旭^1,2

1.兰州理工大学计算机与通信学院,兰州 730050;
2.北京邮电大学信息与通信工程学院,北京 100876

收稿日期:2017-04-13 出版日期:2018-07-01 发布日期:2018-07-01
作者简介:曹洁(1966-),女,教授,博士生导师.研究方向:智能信息处理,信息融合.E-mail:caoj@lut.cn
基金资助:
国家自然科学基金项目(61563030).

Image annotation method based on Corr-LDA model

CAO Jie¹, SU Zhe¹, LI Xiao-xu^1,2

1.School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050,China;
2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876,China

Received:2017-04-13 Online:2018-07-01 Published:2018-07-01

摘要/Abstract

摘要： 针对现有图像标注方法大多将不同类别的图像置于同一主题空间下进行标注的不足,提出了一种新的图像标注方法,该方法以Corr-LDA模型为基础,将各类图像置于不同主题空间下,并为每个类别学习出适合该类的图像标注模型。在Labelme及UIUC-Sport数据集上的实验结果表明,本文方法的标注性能要优于其他方法。

关键词: 计算机应用, 图像标注, 概率主题模型, 变分EM, Corr-LDA模型

Abstract: Most of existing image annotation methods are based on the same topic space. In fact, categories are important information to determine the image annotation words, different classifications of images present different objects. In this paper, a new image annotation method based on Corr-LDA model is proposed. In this method all sorts of images are placed in different topic spaces, and the image annotation model suitable to each category is learned. Experiment results on Labelme and UIUC-Sport datasets show that the proposed method has better performance than other methods.

Key words: computer application, image annotation, probabilistic topic model, variational expectation maximization, Corr-LDA model

中图分类号:

TP391

曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.

CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.

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基于Corr-LDA模型的图像标注方法

Image annotation method based on Corr-LDA model

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