局部自相关函数在基于内容的图像检索中的应用

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 259-262.

局部自相关函数在基于内容的图像检索中的应用

胡志军¹, 刘广海², 苏又¹

广西师范大学数学与统计学院广西桂林541004¹
广西师范大学计算机科学与信息工程学院广西桂林541004²

出版日期:2019-02-26 发布日期:2019-02-26
作者简介:胡志军(1981-),男,硕士,讲师,主要研究方向为分形图像压缩、图像检索;刘广海(1977-),男,博士,教授,主要研究方向为模式识别、图像检索、智能分类和目标检测、计算机应用技术等;苏又(1986-),女,硕士,讲师,主要研究方向为排队论。
基金资助:
本文受广西师范大学自然科学基金项目:基于码本块属性分析缩减码本池的快速分形图像压缩算法,广西教育厅科研项目立项课题:具有两种服务率的带休假和休假中止策略的GI/M/c/m排队模型(KY2015LX013),广西师范大学自然科学基金项目:基于3个可变环境下的M/M/1休假排队模型研究资助。

Application of Local Autocorrelation Function in Content-based Image Retrieval

HU Zhi-jun¹, LIU Guang-hai², SU You¹

College of Mathematics and Statistics,Guangxi Normal University,Guilin,Guangxi 541004,China¹
College of Computer Science and Information Engineering,Guangxi Normal University,Guilin,Guangxi 541004,China²

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 在图像检索领域中,为了更加方便、高效地进行图像检索,文中提出了一种新的图像检索特征——局部自相关特征,为基于内容的图像检索提供了新的工具,它兼具方向特征和纹理特征。利用提出的局部自相关特征在Corel10K图像库上进行了大量的实验,实验结果表明局部自相关特征的平均检索精确度和召回率虽然低于颜色特征,但高于方向特征,是除颜色特征之外又一个高效的图像检索特征。

关键词: HSV颜色空间, 方向特征, 局部自相关特征, 图像检索, 颜色特征, 自相关函数

Abstract: In the field of image retrieval,in order to make the image retrieval more convenient and efficient,this paper proposed a new image retrieval feature,namly local autocorrelation feature,which provides a new tool for content-based image retrieval.It has the characteristics of orientation feature and texture feature.The experiment was carried out for local autocorrelation feature presented in this paper on the Corel10K database,the experimental results show that the average retrieval precision and recall rate of the local autocorrelation feature are lower than the color feature,but it is higher than that of the orientation feature.In addition to color features,the local autocorrelation feature is an efficient image retrieval feature.

Key words: Autocorrelation function, Color feature, Direction feature, HSV color space, Image retrieval, Local autocorrelation feature

中图分类号:

O235

胡志军, 刘广海, 苏又. 局部自相关函数在基于内容的图像检索中的应用[J]. 计算机科学, 2018, 45(11A): 259-262. https://doi.org/

HU Zhi-jun, LIU Guang-hai, SU You. Application of Local Autocorrelation Function in Content-based Image Retrieval[J]. Computer Science, 2018, 45(11A): 259-262. https://doi.org/

参考文献

[1]KOMMINENI J,SATRIA M,SHAHRIZAL S M.Content based image retrieval using colour strings comparison[J].Procedia Computer Science,2015,50:374-379.
[2]GANAR A N,JAMBHULKAR G C S,SACHIN M.Enhancement of image retrieval by using colour,texture and shape fetures[C]∥Proceedings International Conference on Electronic Systems,Signal Processing,and Computing Technologies.2014:251-255.
[3]GHANSHYAM R,VIPIN T.Texture image retrieval using adaptive terolet transforms[J].Digital Signal Processing:A Review Journal,2016,48:50-57.
[4]LIN C H,HSIAO M D,LIN W T.Object-based image segmentation and retrieval for texture images[J].Imaging Science Journal,2015,63(4):220-234.
[5]AZZA G,MEHREZ Z,NICALAS B,et al.Retrieval of both soil moisture and texture using TerraSAR-X images[J].Remote Sensing,2015,7(8):10098-10116.
[6]HUANG M,SHU H Z,MA Y Q.Content-based image retrieval technology using multi-feature fusion[J].Optik,2015,126(19):2144-2148.
[7]LIU G H,YANG J Y,et al.Content-based image retrieval using computational visual attention model[J].Pattern Recognition,2015,48(8):2554-2566.
[8]赵小川,何灏,等.MATLAB数字图像处理实战[M].北京:机械工业出版社,2013.
[9]GONZALEZ R C,WOODS R E,EDDINS S L.数字图像处理[M].阮秋琦,译.北京:电子工业出版社,2005.
[10]LIU G H,YANG J Y.Content-based image retrieval using color difference histogram[J].Pattern Recognition,2013,46(1):188-198.

相关文章 15

[1]	王春静, 刘丽, 谭艳艳, 张化祥. 基于模糊颜色特征和模糊相似度的图像检索方法 Image Retrieval Method Based on Fuzzy Color Features and Fuzzy Smiliarity 计算机科学, 2021, 48(8): 191-199. https://doi.org/10.11896/jsjkx.200800202
[2]	王晓飞, 周超, 刘利刚. 基于特征聚类的轻量级图像搜索系统 Lightweight Image Retrieval System Based on Feature Clustering 计算机科学, 2021, 48(2): 148-152. https://doi.org/10.11896/jsjkx.191200104
[3]	徐行, 孙嘉良, 汪政, 杨阳. 基于特征变换的图像检索对抗防御 Feature Transformation for Defending Adversarial Attack on Image Retrieval 计算机科学, 2021, 48(10): 258-265. https://doi.org/10.11896/jsjkx.200800222
[4]	周先春, 徐燕. 基于结构相关性的自适应图像修复 Adaptive Image Inpainting Based on Structural Correlation 计算机科学, 2020, 47(4): 131-135. https://doi.org/10.11896/jsjkx.190300149
[5]	焦扬, 杨传颖, 石宝. 基于SVM相关反馈的鞋印图像检索算法 Relevance Feedback Method Based on SVM in Shoeprint Images Retrieval 计算机科学, 2020, 47(11A): 244-247. https://doi.org/10.11896/jsjkx.200400032
[6]	孙伟, 赵玉普. 增强旋转不变LBP算法及其在图像检索中的应用 Enhanced Rotation Invariant LBP Algorithm and Its Application in Image Retrieval 计算机科学, 2019, 46(7): 263-267. https://doi.org/10.11896/j.issn.1002-137X.2019.07.040
[7]	王晓, 邹泽伟, 李勃勃, 王静. 基于多特征融合的彩色图像声呐目标检测 Target Detection in Colorful Imaging Sonar Based on Multi-feature Fusion 计算机科学, 2019, 46(6A): 177-181.
[8]	彭金喜, 苏远歧, 薛笑荣. 基于深度学习和同生矩阵的SAR图像纹理特征检索方法 SAR Image Feature Retrieval Method Based on Deep Learning and Synchronic Matrix 计算机科学, 2019, 46(6A): 196-199.
[9]	侯媛媛, 何儒汉, 李敏, 陈佳. 结合卷积神经网络多层特征融合和K-Means聚类的服装图像检索方法 Clothing Image Retrieval Method Combining Convolutional Neural Network Multi-layerFeature Fusion and K-Means Clustering 计算机科学, 2019, 46(6A): 215-221.
[10]	何霞, 汤一平, 王丽冉, 陈朋, 袁公萍. 基于Faster RCNNH的多任务分层图像检索技术 Multitask Hierarchical Image Retrieval Technology Based on Faster RCNNH 计算机科学, 2019, 46(3): 303-313. https://doi.org/10.11896/j.issn.1002-137X.2019.03.045
[11]	李晓雨, 聂秀山, 崔超然, 蹇木伟, 尹义龙. 基于迁移学习的图像检索算法 Image Retrieval Algorithm Based on Transfer Learning 计算机科学, 2019, 46(1): 73-77. https://doi.org/10.11896／j.issn.1002-137X.2019.01.011
[12]	吴鹏, 周杰, 陈姜高路. SOC水声信道模型及其计算方法研究 Research on Underwater Acoustic Channel Model and Its Calculation Method Based on SOC 计算机科学, 2018, 45(8): 94-99. https://doi.org/10.11896/j.issn.1002-137X.2018.08.017
[13]	陈嵘, 李鹏, 黄勇. 基于多特征融合的运动阴影去除算法 Moving Shadow Removal Algorithm Based on Multi-feature Fusion 计算机科学, 2018, 45(6): 291-295. https://doi.org/10.11896/j.issn.1002-137X.2018.06.051
[14]	刘颖, 张帅, 葛瑜祥, 王富平, 李大湘. 轮胎花纹图像检索技术综述 Survey of Tire Pattern Image Retrieval Techniques 计算机科学, 2018, 45(12): 52-60. https://doi.org/10.11896／j.issn.1002-137X.2018.12.007
[15]	李丽萍,赵传荣,孔德仁,王芳. 基于图论的无监督区域遥感图像检索算法研究 Research on Unsupervised Regional Remote Sensing Image Retrieval Algorithm Based on Graph Theory 计算机科学, 2017, 44(7): 315-317. https://doi.org/10.11896/j.issn.1002-137X.2017.07.057

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed