吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 601-608.doi: 10.13229/j.cnki.jdxbgxb201702035

Previous Articles     Next Articles

Image feature description method based on local entropy

ZHOU Bao-yu1, ZHAO Hong-wei1, XIAO Yang2, ZANG Xue-bai1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.College of Electronic Information,Jilin Communications Polytechnic, Changchun 130012, China
  • Received:2015-12-22 Online:2017-03-20 Published:2017-03-20

PDF (PC)

417

Abstract: The traditional model, local scale-invariant features, does not capture some parts of the important information of an image, which will lead to bad matching result. In order to address this problem, extracting feature information from different scales in blurring space is better than the classic algorithm. A local entropy method can estimate interesting point information from its surrounding, which can obtain more image content. This paper provides a new image feature description based on local entropy. First, the orient histograms from different scales within different Gaussians space are computed, and each local entropy value is estimated from each scale level. Then, all of the descriptors of the same interesting point are fussed based on the ratio of each entropy to the whole local entropy value of the same feature in different scale levels. Experiment results demonstrate that, compared with order out-of-state local descriptor, the two descriptors provided in this paper have strong robust under different conditions.

Key words: computer application, image feature detection, image feature description, local entropy

CLC Number: 

  • TP391
[1] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]∥Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, 2005:886-893.
[2] Redondi A, Cesana M, Tagliasacchi M. Low bitrate coding schemes for local image descriptors[C]∥IEEE International Workshop on Multimedia Signal Processing, Canada, 2012:124-129.
[3] Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos[C]∥IEEE International Conference on Computer Vision, Beijing, 2003:1470.
[4] Zhao H, Zhou B, Liu P, et al. Modulating a local shape descriptor through biologically inspired color feature[J]. Journal of Bionic Engineering, 2014,11(2): 311-321.
[5] Lowe D G. Object recognition from local scale-invariant features[C]∥IEEE International Conference on Computer Vision,Toronto, 1999:91-110.
[6] Bay H, Ess A, Tuytelaars T. Speeded-up robust features (SURF)[J]. Computer Vision & Image Understanding, 2008, 110(3): 346-359.
[7] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[8] Mikolajczyk K, Schmid C. An affine invariant interest point detector[J]. European Conference on Computer Vision, 2002, 1(10):128-142.
[9] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
[10] Wang Z, Fan B, Wu F. Local intensity order pattern for feature description[J]. IEEE International Conference on Computer Vision,2011, 23(5):603-610.
[11] Tola E, Lepetit V, Fua P. Daisy: an efficient dense descriptor applied to wide-baseline stereo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 815-30.
[12] Hassner T, Mayzels V, Zelnik-Manor L. On SIFTs and their scales[J]. IEEE Conference on Computer Vision & Pattern Recognition, 2012, 157(10):1522-1528.
[13] Wang Z, Fan B, Wu F. Affine subspace representation for feature description[R]. Zurich:Lecture Notes in Computer Science,2014.
[14] Dong J, Soatto S. Domain-size pooling in local descriptors: DSP-SIFT[J]. Eprint Arxiv, 2015:5097-5106.
[15] Yang T Y, Lin Y Y, Chuang Y Y. Accumulated stability voting: a robust descriptor from descriptors of multiple scales[C]∥Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016:327-335.
[16] Shannon C E. A mathematical theory of communication[J]. ACM Sigmobile Computing and Communications Review, 2001, 5(1): 3-55.
[17] Kadir T, Brady M. Saliency, scale and image description[J]. International Journal of Computer Vision, 2001, 45(2): 83-105.
[18] Kapur J N, Sahoo P K, Wong A K. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics, and Image Processing, 1985, 29(3): 273-285.
[19] Chen X, Zhao H, Liu P. Automatic salient object detection via maximum entropy estimation[J]. Optics Letters, 2013, 38(10): 1727-1729.
[20] Lindeberg T. On scale selection for differential operators[C]∥Proc Scandinavian Conference on Image Analysis, Tromssa, Norway, 1993:317-348.
[21] Lindeberg T. Junction detection with automatic selection of detection scales and localization scales[C]∥IEEE International Conference,Austin,2012:924-928.
[22] Tuzel O, Porikli F, Meer P. Region covariance: a fast descriptor for detection and classification[C]∥Computer Vision - ECCV 2006, European Conference on Computer Vision, Graz, Austria, 2006:589-600.
[23] Lindeberg T. Feature detection with automatic scale selection[J]. International Journal of Computer Vision, 1998, 30(2): 79-116.
[24] Yang Lei,Ren Yan-yun, Zhang Wen-qiang.3D depth image analysis for indoor fall detection of elderly people[J].Digital Communications & Networks ,2016,2(1):24-34
[25] Wu Wen-qi ,WangXin-gang , Huang Guan,et al.Automatic gear sorting system based on monocular vision[J].Digital Communications & Networks ,2015,1(4):284-291
[26] Xu Han-song ,Hua Kun,Wang Hong-gang.Adaptive FEC coding and cooperative relayed wireless image transmission[J].Digital Communications & Networks ,2015,1(3):213-221
[27] Arandjelovic ' R, Zisserman A. Three things everyone should know to improve object retrieval[J].Computer Vision & Pattern Recognition, 2012, 157(10):2911-2918.
[28] Hua G, Brown M, Winder S. Discriminant Embedding for Local Image Descriptors[C]∥IEEE 11th International Conference on Computer Vision, Brazil, 2007:1-8.
[29] Vedaldi A, Fulkerson B. Vlfeat: an open and portable library of computer vision algorithms[C]∥International Conference on Multimedea,New York,2010:1469-1472.
[1] LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[2] WANG Li-min,LIU Yang,SUN Ming-hui,LI Mei-hui. Ensemble of unrestricted K-dependence Bayesian classifiers based on Markov blanket [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1851-1858.
[3] JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.
[4] ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.
[5] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[6] OUYANG Dan-tong, FAN Qi. Clause-level context-aware open information extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1563-1570.
[7] LIU Fu, LAN Xu-teng, HOU Tao, KANG Bing, LIU Yun, LIN Cai-xia. Metagenomic clustering method based on k-mer frequency optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1593-1599.
[8] GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[9] LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10] CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[11] ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] FU Wen-bo, ZHANG Jie, CHEN Yong-le. Network topology discovery algorithm against routing spoofing attack in Internet of things [J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] HOU Yong-hong, WANG Li-wei, XING Jia-ming. HTTP-based dynamic adaptive streaming video transmission algorithm [J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] ZHANG Lin, ZHAO Hong-wei, YANG Yi-han, MA Zhi-chao, HUANG Hu, MA Zhi-chao. Molecular dynamics simulation of nanoindentation of single-layer graphene sheet[J]. 吉林大学学报(工学版), 2013, 43(06): 1558 -1565 .
[2] SHI Wen-ku, LIU Guo-zheng, SONG Hai-sheng, CHEN Zhi-yong, ZHANG Bao. Vibration and noise characteristics of electric bus[J]. 吉林大学学报(工学版), 2018, 48(2): 373 -379 .
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd