山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 8-16.doi: 10.6040/j.issn.1672-3961.0.2018.271
Hongbin ZHANG1(),Diedie QIU1,Renzhong WU1,Tao ZHU2,Jin HUA2,Donghong JI3
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。
中图分类号:
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