使用兴趣点局部分布特征及多示例学习的图像检索方法

doi:10.3969/j.issn.1001-2400.2011.02.009

Abstract

Abstract:

A novel method for image retrieval based on interest points is presented. The interest points are detected in the scale space. Then the image is divided into fan-shaped sub-regions of equal area according to the distribution of the interest points. Local features representing the spatial distribution information on the interest points are extracted to describe the image, and they are also robust to the image's rotation, scale and translation. In the relevant feedback, images are regarded as multiple-instance bags consisting of the local domain of the interest points in every fan-shaped sub-region. Labeled images chosen by the user are generated corresponding positive and negative bags, and the multiple-instance learning algorithm is employed to obtain the target concept reflecting the query image semantics. The method can reduce the ambiguity of the user query. Experimental results based on the Core image database show that our method improves the average retrieval precision by 7 percent or more, compared with other interest points based retrieval methods.

Key words: image retrieval, interest points, feature extraction, local distribution features, multiple-instance learning

CLC Number:

TP391.41

MENG Fanjie;GUO Baolong. Image retrieval by using local distribution features of interest points and multiple-instance learning[J].J4, 2011, 38(2): 47-53.

References

［1］ Rahmani R, Goldman S A, Hui Zhang, et al. Localized Content Based Image Retrieval ［C］//Proc of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval. New York: ACM, 2005: 227-236.
［2］ Zeng Zhiyong, Liu Shigang. A Novel Region-based Image Retrieval Algorithm Using Hybrid Feature［C］//WRI World Congress on Computer Science and Information Engineering. Los Angeles: IEEE Computer Society, 2009:416-420.
［3］ Chiang Cheng Chieh, Hung Yi Ping, Yang Hsuan, et al. Region-based Image Retrieval Using Color-size Features of Watershed Regions ［J］. Journal of Visual Communication and Image Representation, 2009, 20(3):167-177.
［4］ Wol C, Jolion J, Kropatsch W, et al. Content Based Image Retrieval Using Interest Points and Texture Features［C］//Proc of 15th International Conference on Pattern Recognition. Barcelona: IAPR, 2000: 234-237.
［5］孟繁杰,郭宝龙.一种基于兴趣点颜色及空间分布的图像检索方法［J］.西安电子科技大学学报, 2005, 32(2): 256-259.
Meng Fanjie,Guo Baolong. A Novel Image Retrieval Algorithm Based on the Color and Distribution of Interest Points ［J］. Journal of Xidian University, 2005, 32(2):256-259.
［6］ Zheng Xia, Zhou Mingquan, Wang Xingce. Interest Point Based Medical Image Retrieval ［C］//Lecture Notes in Computer Science. Beijing: Springer Verlag, 2008:118-124.
［7］ Jian Muwei, Chen Shi. Image Retrieval Based on Clustering of Salient Points ［C］//Proc of 2008 2nd International Symposium on Intelligent Information Technology Application. Shanghai: Inst of Elec and Elec Eng Computer Society, 2008: 347-351.
［8］苏小红,丁进,马培军.用兴趣点凸包和SVM加权反馈实现图像检索［J］.计算机学报, 2009,32(11):2221-2228.
Su Xiaohong, Ding Jin, Ma Peijun. Image Retrieval by Convex Hulls of Interest Points and SVM-Based Weighted Feedback ［J］.Chinese Journal of Computers, 2009,32(11):2221-2228.
［9］符祥,曾接贤.基于兴趣点匹配和空间分布的图像检索方法［J］.中国激光,2010,37(3):774-778.
Fu Xiang, Zeng Jiexian. A Novel Image Retrieval Method Based on Interest Points Matching and Distribution ［J］.Chinese Journal of Lasers, 2010, 37(3):774-778.
［10］ Schmid C, Mohr R. Local Grayvalue Invariants for Image Retrieval ［J］. IEEE Trans on PAMI, 1997, 19(5): 530-535.
［11］ Bas P, Chassery J M, Macq B. Geometrically Invariant Watermarking Using Feature Points ［J］. IEEE Trans on Image Processing, 2002, 11(9):1014-1028.
［12］ Mikolajczyk K, Schmid C. Scale and Affine Invariant Interest Point Detectors ［J］. International Journal of Computer Vision, 2004, 60(1): 63-86.
［13］ Wu Jianhua, Wei Zhaorong, Chang Youli. Color and Texture Feature for Content Based Image Retrieval ［J］. International Journal of Digital Content Technology and its Applications, 2010, 4(3):43-49.
［14］ Dietterich T G, Lathrop R H, Lozano-Perez T. Solving the Multiple-instance Problem with Axis-parallel Rectangles ［J］. Artificial Intelligence, 1997, 89(1-2): 31-71.
［15］ Zhang Q, Goldman S A. EM-DD: an Improved Multiple-instance Learning Technique［C］//Advances in Neural Information Processing Systems 14. Cambridge: MIT Press, 2002:1073-1080.

[1]	YAN Jia,CAO Yudong,REN Jiaxing,CHEN Donghao,LI Xiaohui. Deep asymmetric compression Hashing algorithm [J]. Journal of Xidian University, 2021, 48(5): 212-221.
[2]	LIU Jieyi,GONG Maoguo,ZHAN Tao,LI Hao,ZHANG Mingyang. Method for discrimination of false targets in multistation radar systems based on the deep neural network [J]. Journal of Xidian University, 2021, 48(2): 133-138.
[3]	WEI Ziyu,YANG Xi,WANG Nannan,YANG Dong,GAO Xinbo. Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification [J]. Journal of Xidian University, 2021, 48(2): 205-212.
[4]	GUO Zekun,TIAN Long,HAN Ning,WANG Penghui,LIU Hongwei,CHEN Bo. Radar HRRP based few-shot target recognition with CNN-SSD [J]. Journal of Xidian University, 2021, 48(2): 7-14.
[5]	HAN Zhuoxi,WANG Feng,CHEN Pei,LI Zhuolun. Fuzzy data association algorithm assisted by historical features [J]. Journal of Xidian University, 2021, 48(2): 92-98.
[6]	WANG Xiaohua,YAO Pengchao,MA Liping,WANG Wenjie,ZHANG Lei. Algorithm for extraction of features of robot speech control in the factory environment [J]. Journal of Xidian University, 2020, 47(2): 16-22.
[7]	WANG Hongyan,ZHU Limin,ZHANG Panjie,LI Jinping. Method for the detection of the piston side defect based on external contour registration [J]. Journal of Xidian University, 2019, 46(5): 75-83.
[8]	GUO Rui,ZANG Bo,PENG Shuming,XING Mengdao. Extraction of features of the urban high-rise building from high resolution InSAR data [J]. Journal of Xidian University, 2019, 46(4): 137-143.
[9]	LIU Daohua,CUI Yushuang,ZHAO Yansong,SONG Yuting,WANG Jinghui. Method for retrieving the teaching image based on the improved convolutional neural network [J]. Journal of Xidian University, 2019, 46(3): 52-58.
[10]	YANG Jiangong,WANG Xili,LIU Shigang. Spectral-spatial classification of hyperspectral images using deep Boltzmann machines [J]. Journal of Xidian University, 2019, 46(3): 109-115.
[11]	WANG Xinyuan,XIAO Song,LI Lei,JIAO Lingling. Robust target tracking algorithm based on the ELM and discriminative correlation filter [J]. Journal of Xidian University, 2019, 46(1): 57-63.
[12]	WANG Xing;HUANG Xiaoyu;LIU Xuanpu;KONG Xianguang;NIU Meng. Multi-layer incremental feature extraction method for industrial big data [J]. Journal of Xidian University, 2018, 45(4): 106-111.
[13]	HE Wangpeng;SUN Wei;SU Bo;YANG Yunyi;GUO Baolong. Sparse feature extraction technique and its applications to machinery fault diagnosis [J]. Journal of Xidian University, 2018, 45(2): 154-159.
[14]	ZHANG Yuhe;GENG Guohua;WEI Xiaoran;SHI Chenchen;ZHANG Shunli. Feature extraction of point clouds using the DBSCAN clustering [J]. Journal of Xidian University, 2017, 44(2): 114-120.
[15]	YANG Yuan;ZHA Yufei;QIN Bing;LIANG Bingbing;YANG Liwei;LI Yunqiang. Image retrieval based on kernel trick and iterative quantization [J]. Journal of Xidian University, 2017, 44(1): 165-170.

Image retrieval by using local distribution features of interest points and multiple-instance learning

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10