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

J4 ›› 2011, Vol. 38 ›› Issue (2): 47-53.doi: 10.3969/j.issn.1001-2400.2011.02.009

使用兴趣点局部分布特征及多示例学习的图像检索方法

孟繁杰;郭宝龙

(西安电子科技大学智能控制与图像工程研究所，陕西西安 710071)

收稿日期:2010-12-28 出版日期:2011-04-20 发布日期:2011-05-26
通讯作者: 孟繁杰
作者简介:孟繁杰(1978-)，女，讲师，西安电子科技大学博士研究生，E-mail: Chinesemeng@sina.com．
基金资助:
国家自然科学基金资助项目(61003196);中央高校基本科研业务费专项资金资助项目(K50510040004)

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

MENG Fanjie;GUO Baolong

(Inst. of Intelligent Control ＆ Image Engineering, Xidian Univ., Xi'an 710071， China)

Received:2010-12-28 Online:2011-04-20 Published:2011-05-26
Contact: MENG Fanjie

摘要/Abstract

摘要：

提出了一种基于兴趣点的图像检索新方法．在尺度空间中检测兴趣点，依据兴趣点的分布将图像划分成一系列等面积的扇形子区域并提取图像特征．该特征既反映了兴趣点的局部特性，又考虑了兴趣点的空间分布结构，同时对图像旋转、缩放和平移具有不变性．在相关反馈阶段，将图像看作是由各子区域内兴趣点局部特征构成的多示例包，根据用户选择的实例图像生成正包和反包，采用多示例学习算法获得体现图像语义的目标概念．本方法缩小了用户查询中的歧义性，在Corel图像库中进行的实验表明，与其他基于兴趣点的图像检索方法相比，平均检索准确率提高7%以上，可以更准确地查找到用户所需图像．

关键词: 图像检索, 兴趣点, 特征提取, 局部分布特征, 多示例学习

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

中图分类号:

TP391.41

孟繁杰;郭宝龙. 使用兴趣点局部分布特征及多示例学习的图像检索方法[J]. J4, 2011, 38(2): 47-53.

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.

参考文献

［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]	闫佳,曹玉东,任佳兴,陈冬昊,李晓会. 深度非对称压缩型哈希算法[J]. 西安电子科技大学学报, 2021, 48(5): 212-221.
[2]	刘洁怡,公茂果,詹涛,李豪,张明阳. 一种深度神经网络多站雷达系统干扰鉴别方法[J]. 西安电子科技大学学报, 2021, 48(2): 133-138.
[3]	魏梓钰,杨曦,王楠楠,杨东,高新波. 互惠双向生成对抗网络用于跨模态行人重识别[J]. 西安电子科技大学学报, 2021, 48(2): 205-212.
[4]	郭泽坤,田隆,韩宁,王鹏辉,刘宏伟,陈渤. 采用CNN-SSD的雷达HRRP小样本目标识别方法[J]. 西安电子科技大学学报, 2021, 48(2): 7-14.
[5]	韩卓茜,王锋,陈沛,李卓伦. 一种历史特征辅助的模糊数据关联算法[J]. 西安电子科技大学学报, 2021, 48(2): 92-98.
[6]	王晓华,要鹏超,马丽萍,王文杰,张蕾. 车间环境下机器人语音控制的特征提取算法[J]. 西安电子科技大学学报, 2020, 47(2): 16-22.
[7]	王红艳,朱利民,张潘杰,李金屏. 一种利用外轮廓配准的活塞侧面缺陷检测方法[J]. 西安电子科技大学学报, 2019, 46(5): 75-83.
[8]	郭睿,臧博,彭树铭,邢孟道. 高分辨InSAR中的城市高层建筑特征提取[J]. 西安电子科技大学学报, 2019, 46(4): 137-143.
[9]	刘道华,崔玉爽,赵岩松,宋玉婷,王景慧. 一种改进卷积神经网络的教学图像检索方法[J]. 西安电子科技大学学报, 2019, 46(3): 52-58.
[10]	杨建功,汪西莉,刘侍刚. 融合谱-空域信息的DBM高光谱图像分类方法[J]. 西安电子科技大学学报, 2019, 46(3): 109-115.
[11]	王欣远,肖嵩,李磊,焦玲玲. 融合ELM和相关滤波的鲁棒性目标跟踪算法[J]. 西安电子科技大学学报, 2019, 46(1): 57-63.
[12]	汪星;黄小瑜;刘瑄璞;孔宪光;牛萌. 面向工业大数据的多层增量特征提取方法[J]. 西安电子科技大学学报, 2018, 45(4): 106-111.
[13]	贺王鹏;孙伟;苏博;闫允一;郭宝龙. 机械故障诊断的稀疏特征提取方法[J]. 西安电子科技大学学报, 2018, 45(2): 154-159.
[14]	张雨禾;耿国华;魏潇然;石晨晨;张顺利. 采用密度空间聚类的散乱点云特征提取方法[J]. 西安电子科技大学学报, 2017, 44(2): 114-120.
[15]	杨源;查宇飞;覃兵;梁兵兵;杨丽薇;李运. 利用核技巧和迭代量化的图像检索算法[J]. 西安电子科技大学学报, 2017, 44(1): 165-170.

使用兴趣点局部分布特征及多示例学习的图像检索方法

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

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10