2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于形式概念分析和语义关联规则的目标图像标注

顾广华 曹宇尧 崔冬 赵耀

顾广华, 曹宇尧, 崔冬, 赵耀. 基于形式概念分析和语义关联规则的目标图像标注. 自动化学报, 2020, 46(4): 767-781. doi: 10.16383/j.aas.c180523
引用本文: 顾广华, 曹宇尧, 崔冬, 赵耀. 基于形式概念分析和语义关联规则的目标图像标注. 自动化学报, 2020, 46(4): 767-781. doi: 10.16383/j.aas.c180523
GU Guang-Hua, CAO Yu-Yao, CUI Dong, ZHAO Yao. Object Image Annotation Based on Formal Concept Analysis and Semantic Association Rules. ACTA AUTOMATICA SINICA, 2020, 46(4): 767-781. doi: 10.16383/j.aas.c180523
Citation: GU Guang-Hua, CAO Yu-Yao, CUI Dong, ZHAO Yao. Object Image Annotation Based on Formal Concept Analysis and Semantic Association Rules. ACTA AUTOMATICA SINICA, 2020, 46(4): 767-781. doi: 10.16383/j.aas.c180523

基于形式概念分析和语义关联规则的目标图像标注

doi: 10.16383/j.aas.c180523
基金项目: 

国家自然科学基金 61303128

河北省自然科学基金 F2017203169

河北省自然科学基金 F2018203239

河北省高等学校科学研究重点项目 ZD2017080

河北省留学回国人员科技活动项目 CL201621

详细信息
    作者简介:

    曹宇尧  燕山大学信息科学与工程学院硕士研究生. 2016年获得燕山大学电子信息工程专业学士学位.主要研究方向为多语义图像识别, 形式概念分析.E-mail: cyg19921129@163.com

    崔冬  燕山大学信息科学与工程学院副教授. 2011年获得燕山大学检测技术与自动化装置专业博士学位.主要研究方向为医学信号处理.E-mail: cuidong@ysu.edu.cn

    赵耀  北京交通大学信息科学研究所教授.1996年获得北京交通大学信号与信息处理专业博士学位.主要研究方向为多媒体技术.E-mail:yzhao@bjtu.edu.cn

    通讯作者:

    顾广华  燕山大学信息科学与工程学院教授. 2013年获得北京交通大学信号与信息处理专业博士学位.主要研究方向为图像理解, 图像检索.本文通信作者.E-mail: guguanghua@ysu.edu.cn

  • 本文责任编委 黄庆明

Object Image Annotation Based on Formal Concept Analysis and Semantic Association Rules

Funds: 

Natural Science Foundation of China 61303128

Natural Science Foundation of Hebei Province F2017203169

Natural Science Foundation of Hebei Province F2018203239

Key Foundation of Hebei Educational Committee ZD2017080

Science and Technology Foundation for Returned Overseas People of Hebei Province CL201621

More Information
    Author Bio:

    CAO Yu-Yao   Master student at the School of Information Science and Engineering at Yanshan University. He received his bachelor degree from Yanshan University in 2016. His research interest covers multi-semantic image recognition and formal concept analysis

    CUI Dong   Associate professor at the School of Information Science and Engineering, Yanshan University. She received her Ph. D. degree in detection technology and automation equipment from Yanshan University in 2011. Her main research interest is medical signal processing

    ZHAO Yao   Professor at the Institute of Information Science at Beijing Jiaotong University. He received his Ph. D. degree in signal and information processing from Beijing Jiaotong University in 1996. His main research interest is multimedia technology

    Corresponding author: GU Guang-Hua   Professor at the School of Information Science and Engineering at Yanshan University. He received the Ph. D. degree in Signal and Information Processing from Beijing Jiaotong University in 2013. His research interest covers image understanding and image retrieval. Corresponding author of this paper
  • Recommended by Associate Editor HUANG Qing-Ming
  • 摘要: 基于目标的图像标注一直是图像处理和计算机视觉领域中一个重要的研究问题.图像目标的多尺度性、多形变性使得图像标注十分困难.目标分割和目标识别是目标图像标注任务中两大关键问题.本文提出一种基于形式概念分析(Formal concept analysis, FCA)和语义关联规则的目标图像标注方法, 针对目标建议算法生成图像块中存在的高度重叠问题, 借鉴形式概念分析中概念格的思想, 按照图像块的共性将其归成几个图像簇挖掘图像类别模式, 利用类别概率分布判决和平坦度判决分别去除目标噪声块和背景噪声块, 最终得到目标语义簇; 针对语义目标判别问题, 首先对有效图像簇进行特征融合形成共性特征描述, 通过分类器进行类别判决, 生成初始目标图像标注, 然后利用图像语义标注词挖掘语义关联规则, 进行图像标注的语义补充, 以避免挖掘类别模式时丢失较小的语义目标.实验表明, 本文提出的图像标注算法既能保证语义标注的准确性, 又能保证语义标注的完整性, 具有较好的图像标注性能.
    Recommended by Associate Editor HUANG Qing-Ming
    1)  本文责任编委 黄庆明
  • 图  1  根据形式背景构造概念格

    Fig.  1  Construction of concept lattice based on the context

    图  2  由概念格构建图像簇

    Fig.  2  Image cluster construction from concept lattice

    图  3  特征融合模型

    Fig.  3  Feature fusion model

    图  4  目标图像簇和背景图像簇的平坦度对比

    Fig.  4  Flatness comparison of object image clusters and background image clusters

    图  5  基于类别模式和特征融合的图像标注

    Fig.  5  Image annotation based on category pattern and feature fusion

    图  6  目标建议算法生成的图像局部框

    Fig.  6  Local blocks generated by the objectness proposal generation algorithm

    图  7  基于关联规则的语义补充

    Fig.  7  Semantic complement based on association rules

    图  8  $d$与算法时间复杂度O${(d)}$的关系

    Fig.  8  The relationship between $d$ and time complexity O${(d)}$

    图  9  平坦度阈值$th$对实验性能的影响

    Fig.  9  Performance on the flatness threshold $th$

    图  10  图像标注方法准确率比较

    Fig.  10  Precision comparison of several annotation methods

    表  1  CNN特征稀疏二值化分类效果对比

    分类精度 CNN$-$10 CNN$-$20 CNN$-$30 CNN$-$40 CNN$-$50 CNN
    Accuracy 0.81 0.88 0.86 0.89 0.93 0.93
    下载: 导出CSV

    表  2  不同图像块的类决策分布

    Table  2  Category decision distribution of different image blocks

    下载: 导出CSV

    表  3  图像簇种类划分

    Table  3  Dividing image cluster types

    下载: 导出CSV

    表  4  融合特征决策值

    Table  4  Decision values of fusion feature

    下载: 导出CSV

    表  5  参数${\beta}$对实验性能的影响

    Table  5  Performance on parameter ${\beta}$

    ${\beta}$ 2 3 4 5 6 7 8
    Silhouette 0.16 0.232 0.33 0.63 0.662 0.72 0.761
    ${Mc(\beta)}$ 6 5.4 5.1 4.9 4.2 3.4 2.8
    下载: 导出CSV

    表  6  ${supp}_{\min}$对实验性能的影响

    Table  6  Performance on parameter ${supp}_{ \min}$

    ${supp}_{ \min}$ $2\times10^{-4}$ $1\times10^{-3}$ $2\times10^{-3}$ $3\times10^{-3}$ $4\times10^{-3}$
    O$({ supp}_{ \min})$ 2.04 0.57 0.31 0.19 0.16
    ${N}({ supp}_{ \min})$ 281 135 96 75 73
    下载: 导出CSV

    表  7  ${conf}_{\min}$对实验性能的影响

    Table  7  Performance on parameter ${conf}_{ \min}$

    ${conf}_{ \min}$ 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
    ${N}({conf}_{ \min})$ 122 87 71 55 37 16 10 5
    ${P}({conf}_{ \min})$ 0.46 0.54 0.62 0.72 0.84 0.86 0.94 0.98
    下载: 导出CSV

    表  8  三种聚类算法的比较

    Table  8  Comparison of three clustering algorithms

    PMC $k$-means AP
    ${o}({ ct})$ 3.20 1.58 1.31
    Silhouette 0.68 0.33 0.39
    下载: 导出CSV

    表  9  VOC 2007数据集中部分语义关联规则

    Table  9  Partial semantic association rules in the VOC 2007 data set

    存在语义 bicycle diningtable bicycle, bus bottle, chair pottedplant, bottle
    关联语义 persion chair person diningtable person
    相关度 0.68 0.70 0.92 0.67 0.70
    下载: 导出CSV

    表  10  三种特征融合方式对比

    Table  10  Comparison of three feature fusion methods

    ${P}$ ${R}$ ${F}$
    最大值融合 0.59 0.61 0.60
    均值融合 0.64 0.58 0.61
    组合融合 0.72 0.56 0.63
    下载: 导出CSV

    表  11  标注实验结果比对

    Table  11  Comparison of annotation results

    ${P}$ ${R}$ ${F}$ O${(t)}$
    IBA 0.44 0.72 0.55 10.94
    IBFA 0.46 0.75 0.57 11.46
    ICFA 0.72 0.56 0.63 10.51
    ICFA + SC 0.72 0.62 0.67 11.74
    下载: 导出CSV

    表  12  图像标注示例

    Table  12  Annotation examples

    下载: 导出CSV
  • [1] Duygulu P, Barnard K, Freitas J F G D, Forsyth D A. Object Recognition as Machine Translation:Learning a Lexicon for a Fixed Image Vocabulary. Berlin: Springer, 2002. 97-112
    [2] Qu S, Xi Y, Ding S. Visual attention based on long-short term memory model for image caption generation. In: Proceedings of the 2017 the Chinese Control and Decision Conference. Chongqing, China: IEEE, 2017. 4789-4794
    [3] Lin T Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA: IEEE Computer Society, 2017. 2117-2125
    [4] Ren S, He K, Girshick R, Sun J. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149 doi: 10.5555/2969239.2969250
    [5] Wang J, Yang Y, Mao J, Huang Z H. CNN-RNN: a unified framework for multi-label image classification. In: Proceedings of Computer Vision and Pattern Recognition. Las Vegas, Nevada, USA: IEEE, 2016. avXiv: 1604.04573
    [6] Tang J, Li H, Qi G J, Chua T S. Image annotation by graph-based inference with integrated multiple single instance representations. IEEE Transactions on Multimedia, 2010, 12(2): 131-141 doi: 10.1109/TMM.2009.2037373
    [7] Wu B, Jia F, Liu W, Ghanem B, Lyu S. Multi-label learning with missing labels using mixed dependency graphs. International Journal of Computer Vision, 2018, 126(8): 875-896 doi: 10.1007/s11263-018-1085-3
    [8] Kong X, Wu Z, Li L J, Zhang R, Yu P S, Wu H, et al. Large-scale multi-label learning with incomplete label assignments. ArXiv preprint, 2014
    [9] Jin C, Jin S W. Image distance metric learning based on neighborhood sets for automatic image annotation. Journal of Visual Communication & Image Representation, 2016, 34(C): 167-175 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3bb87385164eec73ad990f117c7224fa
    [10] Chen Y, Zhu L, Yuille A, Zhang H. Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation, and recognition using knowledge propagation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(10): 1747-1761 https://www.ncbi.nlm.nih.gov/pubmed/19696447
    [11] Yang C, Dong M. Region-based image annotation using asymmetrical support vector machine-based multi-instance learning. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 17-22
    [12] Uijlings J R R, van de Sande K E A, Gevers T, Smeulders A W M. Selective search for object recognition. International Journal of Computer Vision, 2013, 104(2): 154-171 doi: 10.1007/s11263-013-0620-5
    [13] Cheng M M, Zhang Z, Lin W Y, Torr P. Bing: binarized normed gradients for objectness estimation at 300 fps. In: Proceedings of the Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014. 3286-3293
    [14] Felzenszwalb P F, Mcallester D A, Ramanan D. A discriminatively trained, multiscale, deformable part model. In: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). Anchorage, Alaska, USA: IEEE, 2008. 24-26
    [15] Moran S, Lavrenko V. A sparse kernel relevance model for automatic image annotation. International Journal of Multimedia Information Retrieval, 2014, 3(4): 209-229 doi: 10.1007/s13735-014-0063-y
    [16] Wille R. Restructuring lattice theory: an approach based on hierarchies of concepts. Orderd Sets D Reidel, 1982, 83: 314-339 doi: 10.1007%2F978-94-009-7798-3_15
    [17] Thomas, J. Cook, K. A visual analytics agenda. IEEE Transactions on Computer Graphics and Applications, 2006, 26(1): 12-19 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_8e2e1226ca7727d15769e97e76f9ebce
    [18] Tsoumakas G, Katakis I, Vlahavas I. Mining Multi-Label Data. US: Springer, 2010: 667-685
    [19] Yang J, Yang F, Wang G, Li M. Multi-channel and multi-scale mid-level image representation for scene classification. Journal of Electronic Imaging, 2017, 26(2): 023018 doi: 10.1117/1.JEI.26.2.023018
    [20] Girish K, Premraj V, Ordonez V, Dhar S, Li S, Choi Y. Baby talk: understanding and generating simple image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12): 2891-2903 doi: 10.1109/TPAMI.2012.162
    [21] Jia X, Shen L, Zhou X, Yu S. Deep convolutional neural network based HEp-2 cell classification. In: Proceedings of International Conference on Pattern Recognition. Cancun, Mexico: IEEE, 2017
    [22] Rajkomar A, Lingam S, Taylor A G, Blum M, Mongan J. High-throughput classification of radiographs using deep convolutional neural networks. Journal of Digital Imaging, 2017, 30(1): 95-101 doi: 10.1007/s10278-016-9914-9
    [23] Bai C, Huang L, Pan X, Zheng J, Chen S. Optimization of deep convolutional neural network for large scale image retrieval. Neurocomputing, 2018: S0925231218304648 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=5b44c6a8f6eed1303e3763295c7b3d61
    [24] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2014, 115(3): 211-252 http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214533907/
    [25] Smirnov E A, Timoshenko D M, Andrianov S N. Comparison of regularization methods for imageNet classification with deep convolutional neural networks. AASRI Procedia, 2014, 6: 89-94 doi: 10.1016/j.aasri.2014.05.013
    [26] Razavian A S, Azizpour H, Sullivan J, Carlsson S. CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Workshops, 2014: 512-519
    [27] Pang J, Huang J, Qin L, Zhang W, Qing L, Huang Q. Rotative maximal pattern: a local coloring descriptor for object classification and recognition. Information Sciences, 2017, 405 https://www.sciencedirect.com/science/article/pii/S0020025517306527
    [28] Chen Q, Song Z, Dong J, Huang Z, Hua Y, Yan S. Contextualizing Object Detection and Classification. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado USA: IEEE Computer Society, 2011. 1585-1592
    [29] Lu X, Chen Y, Li X. Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE Transactions on Image Processing, 2018, 1(27): 106-120 https://ieeexplore.ieee.org/document/8048518
  • 加载中
图(10) / 表(12)
计量
  • 文章访问数:  3620
  • HTML全文浏览量:  5391
  • PDF下载量:  118
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-08-01
  • 录用日期:  2018-12-18
  • 刊出日期:  2020-04-24

目录

    /

    返回文章
    返回