ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (5): 1067-1076.doi: 10.7544/issn1000-1239.2017.20150949

• 图形图像 • 上一篇    下一篇

分组排序多特征融合的图像检索方法

刘胜蓝,冯林,孙木鑫,刘洋   

  1. (大连理工大学电子信息与电气工程学部 辽宁大连 116024) (大连理工大学创新实验学院 辽宁大连 116024) (liusl@mail.dlut.edu.cn)
  • 出版日期: 2017-05-01
  • 基金资助: 
    国家自然科学基金项目(61602082,61672130,61370200);中国博士后科学基金面上资助基金项目(2015M581331)

Group and Rank Fusion for Accurate Image Retrieval

Liu Shenglan, Feng Lin, Sun Muxin, Liu Yang   

  1. (Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116024) (School of Innovation Experiment, Dalian University of Technology, Dalian, Liaoning 116024)
  • Online: 2017-05-01

摘要: 在图像检索中,多特征图融合方法大多仅对最近邻域进行融合.当每个特征的近邻图排序结果较差时,融合后的新图难以得到理想的检索效果.为了解决该问题,提出一种新的多特征图融合图像检索方法——分组排序融合(group ranking fusion, GRF),该方法将数据集合中的相似图片划分为图片组,利用相似图片组对近邻图的检索结果进行改进,在保持精度的前提下扩充了融合范围.最后,在3个标准数据集上的实验结果表明:多特征融合方法能够有效地利用多特征图提高图像检索效果.

关键词: 多特征融合, 基于内容的图像检索, 规范最小割, 图学习, 检索重排

Abstract: Single feature is not discriminative to describe informational content of an image, which is always a shortcoming in traditional image retrieval task. It can be seen that one image can be described by different but complemented features. So multi-feature fusion ranking methods should be further researched to improve the ranking list of query in image retrieval. Most existing multi-feature fusion methods only focus on the nearest neighbors in image retrieval. When the ranking result of each neighbor graph is poor, it is hard to get ideal image retrieval result after graph fusion. In order to solve the problem, this paper proposes a novel multi-feature fusion method for image retrieval—GRF(group ranking fusion). The proposed method divides similar images of a data set into the same group, and improves the retrieval result of neighbor graph through similar images group. The GRF method expands the fusion scope in the premise of guaranteeing retrieval precision premise. At last, the experimental results on three standard data sets demonstrate that GRF can effectively utilize multi-feature graph to improve the performance of image retrieval.

Key words: multi-feature fusion, content-bases image retrieval, minimizing normalized cut, graph learning, retrieval re-rank

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