Journal of Central South University

, Volume 25, Issue 2, pp 259–276 | Cite as

Exploiting global and local features for image retrieval

  • Li Li (李莉)
  • Lin Feng (冯林)
  • Jun Wu (吴俊)
  • Mu-xin Sun (孙木鑫)
  • Sheng-lan Liu (刘胜蓝)


Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

Key words

local binary patterns hue saturation value (HSV) color space graph fusion image retrieval 



两种基于多特征融合的图像检索方法具有非常好的性能。 但是, 这两种融合方法存在以下问题: 1) 在颜色空间中直接定义纹理结构的方法会增大对颜色特征的描述; 2) 提取多种特征再重新融合为一个向量的方法, 这种方法将有效的特征和无效的特征直接结合后, 无效的特征会降低检索性能。 针对以上问题, 提出一种新的混合框架用于彩色图像检索, 该框架使用词袋模型(bag-of-visual words, BoW)和颜色强度局部差分模式(color intensity-based local difference patterns, CILDP)分别提取图像的不同特征信息。 同时, 提出的融合框架利用 graph density 的方法将 BoW 和 CILDP 的排序结果进行有效融合, 利用该框架能够提高图像检索的精度。 在Corel-1K 数据库上, 返回 10 幅图像时, 提出的框架的平均精度为86.26%, 分别比 CILDP 和 BoW 提高了大约 6.68%和 12.53%。 在不同数据库上的大量实验也验证了该框架在图像检索上的有效性。


局部二值模式 色调 饱和度 明度 (HSV) 颜色空间 图融合 图像检索 


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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and Technology, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina
  2. 2.School of Innovation and EntrepreneurshipDalian University of TechnologyDalianChina
  3. 3.School of Control Science and Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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