吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 542-548.doi: 10.13229/j.cnki.jdxbgxb201602032

• 论文 • 上一篇    下一篇

特征点显著性约束的图像检索方法

赵宏伟1, 2, 李清亮1, 刘萍萍1, 2, 汤寰宇1   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012;
    2.吉林大学 符号计算与知识工程教育部重点实验室,长春 130012
  • 收稿日期:2014-11-14 出版日期:2016-02-20 发布日期:2016-02-20
  • 通讯作者: 刘萍萍(1979-),女,副教授,博士.研究方向:移动视觉搜索,嵌入式技术.E-mail:liupp@jlu.edu.cn E-mail:zhaohw@jlu.edu.cn
  • 作者简介:赵宏伟(1962-),男,教授,博士生导师.研究方向:智能信息系统与嵌入式技术.E-mail:zhaohw@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61101155); 吉林省自然科学基金项目(201215045,20140101184JC)

Feature saliency constraint based image retrieval method

ZHAO Hong-wei1, 2, LI Qing-liang1, LIU Ping-ping1, 2, TANG Huan-yu1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012,China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Center for Computer Fundamental Education,Jilin University, Changchun 130012,China
  • Received:2014-11-14 Online:2016-02-20 Published:2016-02-20

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摘要： 图像检索的Bag of Words体系存在量化后的视觉词影响局部特征的辨别能力并且缺乏特征之间空间关系的缺点,影响检索效率.针对复杂背景的目标查询,提出了融合显著性信息的图像检索方法和基于显著特征点空间距离比的后验证方法.首先,提取图像显著目标区域,使用空间金字塔模型进行图像检索.然后,利用查询图像与检索图像匹配的显著特征对,计算任意两对显著特征点的距离比,保留满足阈值的比值,并求和,用以重新排序结果图像,得到最终的检索结果.实验结果表明:该方法显著提高了检索的精确度,并减少了几何验证过程的运算时间.

关键词: 图像检索, BoW模型, 空间金字塔, 几何验证, 显著性

Abstract: Objective retrieval plays an important role in daily life, however the model "Bag of words" exists some problems for image retrieval. For example, the quantified visual words reduce the discriminative power of the local features and the model does not capture the spatial relationship among local features, thus affecting the retrieval performance. In order to realize objective retrieval in complex environment, a feature saliency constraint based image retrieval method is proposed. By locating the salient object, this method can enhance the discriminative power of the local features and capture the spatial relationship among local features. First, the salient regions are distilled and the features inside these regions are marked as salient features. Second, the image retrieval is performed with spatial pyramid model using the salient features. Finally, the distance ratio of any two pairs of the features in each image is computed using the pair of salient features of the query image and retrieval image; then, the ratios which meet the threshold are recorded and their average is calculated to obtain the final results. Experimental results show that the proposed method improves the accuracy of the retrieval significantly and reduces the computing time during the geometric verification.

Key words: image retrieval, Bag of Words model, spatial pyramid, geometric verification, saliency

中图分类号: 

  • TP391
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