Computer Science

Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 259-262.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Application of Local Autocorrelation Function in Content-based Image Retrieval

HU Zhi-jun1, LIU Guang-hai2, SU You1   

  1. 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

PDF (PC)

637

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

CLC Number: 

  • O235
[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.
[1] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[2] DOU Zhi, WANG Ning, WANG Shi-jie, WANG Zhi-hui, LI Hao-jie. Sketch Colorization Method with Drawing Prior [J]. Computer Science, 2022, 49(4): 195-202.
[3] WANG Chun-jing, LIU Li, TAN Yan-yan, ZHANG Hua-xiang. Image Retrieval Method Based on Fuzzy Color Features and Fuzzy Smiliarity [J]. Computer Science, 2021, 48(8): 191-199.
[4] ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan. Vehicle Color Recognition in Natural Traffic Scene [J]. Computer Science, 2021, 48(6A): 15-20.
[5] WANG Xiao-fei, ZHOU Chao, LIU Li-gang. Lightweight Image Retrieval System Based on Feature Clustering [J]. Computer Science, 2021, 48(2): 148-152.
[6] XU Xing, SUN Jia-liang, WANG Zheng, YANG Yang. Feature Transformation for Defending Adversarial Attack on Image Retrieval [J]. Computer Science, 2021, 48(10): 258-265.
[7] ZHOU Xian-chun, XU Yan. Adaptive Image Inpainting Based on Structural Correlation [J]. Computer Science, 2020, 47(4): 131-135.
[8] JIAO Yang, YANG Chuan-ying, SHI Bao. Relevance Feedback Method Based on SVM in Shoeprint Images Retrieval [J]. Computer Science, 2020, 47(11A): 244-247.
[9] LI Zong-min, LI Si-yuan, LIU Yu-jie, LI Hua. Sketch-based Image Retrieval Based on Attention Model [J]. Computer Science, 2020, 47(11): 199-204.
[10] SUN Wei, ZHAO Yu-pu. Enhanced Rotation Invariant LBP Algorithm and Its Application in Image Retrieval [J]. Computer Science, 2019, 46(7): 263-267.
[11] WANG Xiao, ZOU Ze-wei, LI Bo-bo, WANG Jing. Target Detection in Colorful Imaging Sonar Based on Multi-feature Fusion [J]. Computer Science, 2019, 46(6A): 177-181.
[12] HOU Yuan-yuan, HE Ru-han, LI Min, CHEN Jia. Clothing Image Retrieval Method Combining Convolutional Neural Network Multi-layerFeature Fusion and K-Means Clustering [J]. Computer Science, 2019, 46(6A): 215-221.
[13] HE Xia, TANG Yi-ping, WANG Li-ran, CHEN Peng, YUAN Gong-ping. Multitask Hierarchical Image Retrieval Technology Based on Faster RCNNH [J]. Computer Science, 2019, 46(3): 303-313.
[14] LI Xiao-yu, NIE Xiu-shan, CUI Chao-ran, JIAN Mu-wei, YIN Yi-long. Image Retrieval Algorithm Based on Transfer Learning [J]. Computer Science, 2019, 46(1): 73-77.
[15] WU Peng, ZHOU Jie, CHENG Jiang-gao-lu. Research on Underwater Acoustic Channel Model and Its Calculation Method Based on SOC [J]. Computer Science, 2018, 45(8): 94-99.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.