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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

• 机器学习与数据挖掘 • 上一篇    下一篇

基于极端梯度提升树算法的图像属性标注

张红斌1(),邱蝶蝶1,邬任重1,朱涛2,滑瑾2,姬东鸿3   

  1. 1. 华东交通大学软件学院，江西 南昌 330013
    2. 华东交通大学信息工程学院，江西 南昌 330013
    3. 武汉大学国家网络安全学院，湖北 武汉 430072
  • 收稿日期:2018-07-04 出版日期:2019-04-20 发布日期:2019-04-19
  • 作者简介:张红斌(1979—)，男，江苏如皋人，副教授，硕士生导师，博士，主要研究方向为图像标注，自然语言处理，机器学习. E-mail:zhanghongbin@whu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61762038);国家自然科学基金资助项目(61741108);国家自然科学基金资助项目(61861016);教育部人文社会科学研究规划基金资助项目(16YJAZH029);教育部人文社会科学研究规划基金资助项目(17YJAZH117)

Image attribute annotation based on extreme gradient boosting algorithm

Hongbin ZHANG1(),Diedie QIU1,Renzhong WU1,Tao ZHU2,Jin HUA2,Donghong JI3   

  1. 1. Software School, East China Jiaotong University, Nanchang 330013, Jiangxi, China
    2. School of Information, East China Jiaotong University, Nanchang 330013, Jiangxi, China
    3. School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2018-07-04 Online:2019-04-20 Published:2019-04-19
  • Supported by:
    国家自然科学基金资助项目(61762038);国家自然科学基金资助项目(61741108);国家自然科学基金资助项目(61861016);教育部人文社会科学研究规划基金资助项目(16YJAZH029);教育部人文社会科学研究规划基金资助项目(17YJAZH117)

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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。

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

Abstract:

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.

Key words: image attribute annotation, eXtreme gradient boosting, transfer learning, weak classifiers, deep semantic

中图分类号: 

  • TP391

图1

图像属性标注的基本框架"

表1

迁移学习前主要模型的标注结果比较"

%
模型PrecisionmaterialAPmaterial Accuracy
Pu Canvas Polyester Nylon
Gist_XGBoost 64.45 57.71 38.27 43.09 50.88 52.54
Gist _GBDT 64.65 56.99 38.37 40.89 50.23 51.46
Gist _NB 43.05 37.91 24.41 30.30 33.92 38.28
Gist _RF 61.00 54.45 37.56 40.87 48.47 50.22
Gist _DT 49.16 39.69 29.11 29.33 36.82 37.39
Gist _KNN 57.65 43.60 34.89 44.46 45.15 45.79
Gist _LR 55.91 41.76 0.00 39.25 34.23 46.19
SIFT_XGBoost 75.89 56.86 41.03 56.54 57.58 60.04
SIFT_GBDT 72.65 55.56 43.45 59.53 57.80 59.83
SIFT_NB 84.27 57.04 37.59 49.29 57.05 57.05
SIFT_RF 69.36 49.87 40.00 53.02 53.06 55.97
SIFT_DT 64.62 37.76 29.78 38.24 42.60 43.52
SIFT_KNN 55.33 39.27 32.74 38.02 41.34 41.90
LBP_XGBoost 66.67 57.42 42.06 44.67 52.71 54.24
LBP_GBDT 64.40 57.65 40.87 42.93 51.46 52.89
LBP_NB 45.99 39.61 32.41 33.45 37.87 39.34
LBP_RF 60.43 51.93 40.00 41.38 48.44 50.40
LBP_DT 49.61 38.35 29.00 30.01 36.74 36.80
LBP_KNN 52.15 45.05 38.79 45.00 45.25 46.38
LBP_LR 60.73 53.33 39.72 41.40 48.80 50.03
VGG16_XGBoost 55.68 49.36 35.58 35.09 43.93 45.01
VGG16_GBDT 53.79 46.69 33.61 34.19 42.07 43.39
VGG16_NB 26.75 0.00 0.00 0.00 6.69 26.75
VGG16_RF 53.63 46.91 32.69 35.58 42.20 43.44
VGG16_DT 42.47 38.80 28.35 26.93 34.14 34.15
VGG16_KNN 41.28 35.19 31.51 33.10 35.27 36.20
VGG16_LR 28.34 0.00 31.44 0.00 14.95 28.89

图2

迁移学习前，主要标注模型的P-R曲线"

图3

NT vs BT vs HT的Accuracy对比, (G (Gist), S (SIFT), L (LBP))"

图4

各种模型组合的Accuracy对比"

图5

本研究结果与主要基线的标注精准度对比"

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