基于多标签判别字典学习的图像自动标注

doi:10.11772/j.issn.1001-9081.2017112650

计算机应用 ›› 2018, Vol. 38 ›› Issue (5): 1294-1298.DOI: 10.11772/j.issn.1001-9081.2017112650

基于多标签判别字典学习的图像自动标注

杨晓玲, 李志清, 刘雨桐

湘潭大学智能计算与信息处理教育部重点实验室, 湖南湘潭 411100

收稿日期:2017-11-08 修回日期:2017-11-19 出版日期:2018-05-10 发布日期:2018-05-24
通讯作者: 杨晓玲
作者简介:杨晓玲(1992-),女(土家族),贵州铜仁人,硕士研究生,CCF会员,主要研究方向:机器学习、计算机视觉、图像标注;李志清(1975-),男,湖南娄底人,副教授,博士,CCF会员,主要研究方向:视感知学习、视觉特征提取、视觉信息挖掘、图像语义标注、图像检索;刘雨桐(1992-),女,湖南岳阳人,硕士研究生,CCF会员,主要研究方向:计算机视觉、神经网络、机器学习。

Automatic image annotation based on multi-label discriminative dictionary learning

YANG Xiaoling, LI Zhiqing, LIU Yutong

Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, Xiangtan Hunan 411100, China

Received:2017-11-08 Revised:2017-11-19 Online:2018-05-10 Published:2018-05-24
Contact: 杨晓玲

摘要/Abstract

摘要： 针对图像自动标注中底层视觉特征与高层语义之间的语义鸿沟问题，在传统字典学习的基础上，提出一种基于多标签判别字典学习的图像自动标注方法。首先，为每幅图像提取多种类型特征，将多种特征组合作为字典学习输入特征空间的输入信息；然后，设计一个标签一致性正则化项，将原始样本的标签信息融入到初始的输入特征数据中，结合标签一致性判别字典和标签一致性正则化项进行字典学习；最后，通过得到的字典和稀疏编码矩阵求解标签稀疏编向量，实现未知图像的语义标注。在Corel 5K数据集上测试其标注性能，所提标注方法平均查准率和平均查全率分别可达到35%和48%；与传统的稀疏编码方法（MSC）相比，分别提高了10个百分点和16个百分点；与距离约束稀疏/组稀疏编码方法（DCSC/DCGSC）相比，分别提高了3个百分点和14个百分点。实验结果表明，所提方法能够较好地预测未知图像的语义信息，与当前几种流行的图像标注方法进行比较，所提方法具有较好的标注性能。

关键词: 图像自动标注, 字典学习, 特征表示, 稀疏编码, 图像检索

Abstract: Concerning the problem of semantic gap between low-level visual features and high-level semantics in automatic image annotation, based on traditional dictionary learning, a multi-label discriminative dictionary learning method was proposed to automatic image annotation. First of all, multiple types of features for each image were extracted, and a combination of a variety of features was used as input information of the input feature space to the dictionary learning. Then, a label consistency regularization term was designed to integrate the label information of the original samples into the initial input feature data, and the dictionary of label consistency and the label consistency regularization term were combined to learn the dictionary. Finally, the label sparse coding vector was obtained by the dictionary and sparse coding matrix to implement the semantic annotation for an unknown image. The performance of the annotation was tested on the Corel 5K data set. The average precision and average recall could reach 35% and 48% respectively, compared with the traditional Sparse Coding Method (MSC), which were increased by 10 percentage points and 16 percentage points respectively, and increased by 3 percentage points and 14 percentage points respectively than the method of Distance Constraint Sparse/Group Sparse Coding (DCSC/DCGSC) for automatic image lableing. Compared with the current image annotation methods, the experimental results show the proposed method can predict the semantic information for an unknown image properly, and has better annotation performance.

Key words: automatic image annotation, dictionary learning, feature representation, sparse coding, image retrieval

中图分类号:

TP391.41

杨晓玲, 李志清, 刘雨桐. 基于多标签判别字典学习的图像自动标注[J]. 计算机应用, 2018, 38(5): 1294-1298.

YANG Xiaoling, LI Zhiqing, LIU Yutong. Automatic image annotation based on multi-label discriminative dictionary learning[J]. Journal of Computer Applications, 2018, 38(5): 1294-1298.

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基于多标签判别字典学习的图像自动标注

Automatic image annotation based on multi-label discriminative dictionary learning

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