摘要
提出基于极端梯度提升树(eXtreme gradient boosting, XGBoost)算法的图像属性标注模型,以改善标注性能:提取图像局部二值模式(local binary patterns, LBP)、灰度纹理空间包络特征(Gist)、尺度不变特征变换(scale invariant feature transform, SIFT)、视觉几何组(visual geometry group, VGG)等特征,以准确刻画图像视觉内容;基于图像特征,采用XGBoost算法集成弱分类器为强分类器,完成图像属性标注;深入挖掘图像属性蕴含的深层语义,构建全新的、层次化的属性表示体系,以贴近人类客观认知;设计迁移学习策略并合理组合分类模型,进一步改善标注性能。试验表明:Gist特征能真实刻画图像视觉内容;执行基础迁移学习后,标注精准度比迁移学习前最优指标提升8.69%;执行混合型迁移学习后,合理组合分类模型,标注精准度比基础迁移学习的最优指标提升17.55%。模型有效地改善图像属性标注精度。
To improve annotation performance, a novel image attribute annotation model based on eXtreme gradient boosting(XGBoost) algorithm was proposed: image features i.e. local binary patterns(LBP), Gist, scale invariant feature transform(SIFT), and visual geometry group(VGG) were extracted respectively to better characterize the key visual content of images. Then the state-of-the-art boosting algorithm called XGBoost was used to design a strong classifier by integrating a group of weaker classifiers. Based on the strong classifier, image attribute annotation was implemented. A lot of valuable deep semantic implied by image attribute was mined in turn to create a novel hierarchical attribute representation mechanism, which was closer to human’s objective cognition. Finally, transfer learning strategy was designed to further improve annotation performance. Experimental results showed that the key visual content of images was truly characterized by the Gist feature. Compared to the best competitor before transfer learning, the accuracy of basic transfer(BT) learning strategy was improved about 8.69%. Compared to the best competitor of BT, the accuracy of hybrid transfer(HT) learning strategy was improved about 17.55%. The annotation accuracy was improved by the presented model.
作者
张红斌
邱蝶蝶
邬任重
朱涛
滑瑾
姬东鸿
ZHANG Hongbin;QIU Diedie;WU Renzhong;ZHU Tao;HUA Jin;JI Donghong(Software School, East China Jiaotong University, Nanchang 330013, Jiangxi, China;School of Information, East China Jiaotong University, Nanchang 330013, Jiangxi, China;School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, Hubei, China)
出处
《山东大学学报（工学版）》
CAS
CSCD
北大核心
2019年第2期8-16,共9页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(61762038
61741108
61861016)
教育部人文社会科学研究规划基金资助项目(16YJAZH029
17YJAZH117)的研究成果
关键词
图像属性标注
极端梯度提升树
迁移学习
弱分类器
深层语义
image attribute annotation
eXtreme gradient boosting
transfer learning
weak classifiers
deep semantic
