基于CCA子空间和GMM的自动图像标注

doi:10.3969/j.issn.1000-3428.2013.06.061

计算机工程 ›› 2013, Vol. 39 ›› Issue (6): 277-282.doi: 10.3969/j.issn.1000-3428.2013.06.061

基于CCA子空间和GMM的自动图像标注

郭玉堂^1,2，韩昌刚¹

(1. 安徽大学计算机科学与技术学院，合肥 230601；2. 合肥师范学院计算机科学与技术系，合肥 230061)

收稿日期:2012-05-07 出版日期:2013-06-15 发布日期:2013-06-14
作者简介:郭玉堂(1962－)，男，教授，主研方向：图像处理，模式识别；韩昌刚，硕士研究生
基金项目:
安徽省自然科学基金资助项目(11040606M134)；安徽省高校自然科学基金资助项目(KJ2103A217)

Automatic Image Annotation Based on CCA Subspace and GMM

GUO Yu-tang ^1,2, HAN Chang-gang ¹

(1. School of Computer Science and Technology, Anhui University, Hefei 230601, China; 2. Department of Computer Science and Technology, Hefei Normal College, Hefei 230061, China)

Received:2012-05-07 Online:2013-06-15 Published:2013-06-14

摘要/Abstract

摘要： 为了避免图像分割，并提高图像标注精度，提出一种基于典型相关分析(CCA)和高斯混合模型(GMM)的自动图像标注方法。利用CCA对图像的全局颜色特征与全局局部二值模式(LBP)纹理特征进行特征融合。使用融合后的语义特征，对每一个关键词建立GMM模型来估计单词类密度，从而在特征子空间中得到每个单词的概率分布。采用贝叶斯分类器确定每个标注词和测试图像的联合概率，运用词间语义关系优化标注结果。实验结果表明，使用该方法后的图像标注性能有了较大程度的改善。

Abstract: In order to improve the accuracy of image annotation without using image segmentation, this paper proposes a new automatic image annotation method based on Canonical Correlation Analysis(CCA) subspace and Gaussian Mixture Model(GMM). Do feature fusion between global color feature and global LBP texture feature by using CCA, establish GMM model to estimate the class density probability of every word using the fused feature, and obtain the probability distribution in feature subspace for each word. The joint probability of each tag with respect to the test image is calculated by Bayesian classifier. And the annotation results are refined using semantic correlation between annotation words. Experimental results show that the annotation performance is greatly improved using the proposed method.

Key words: Canonical Correlation Analysis(CCA), feature fusion, Gaussian Mixture Model(GMM), class density probability, image annotation, subspace

中图分类号:

TP391.41

郭玉堂, 韩昌刚. 基于CCA子空间和GMM的自动图像标注[J]. 计算机工程, 2013, 39(6): 277-282.

GUO Yu-Tang, HAN Chang-Gang. Automatic Image Annotation Based on CCA Subspace and GMM[J]. Computer Engineering, 2013, 39(6): 277-282.

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基于CCA子空间和GMM的自动图像标注

Automatic Image Annotation Based on CCA Subspace and GMM

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