Image Retrieval Based on BoVW and Hashing

DAI Xue-mei; LI Sheng-gang

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Image Retrieval Based on BoVW and Hashing

更新时间：2026-09-04

- Image Retrieval Based on BoVW and Hashing
- Vol. 33, Issue 12, Pages: 11-14(2016)
- 作者机构：
  
  1. 陕西师范大学数学与信息科学学院,陕西,西安,710062
  2. 青海师范大学机构数学系,青海,西宁,810008
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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代雪梅, 李生刚. 基于哈希技术的BoVW图像检索[J]. 微电子学与计算机, 2016, 33(12): 11-14+19.

DAI Xue-mei, LI Sheng-gang. Image Retrieval Based on BoVW and Hashing[J]. 2016, 33(12): 11-14.
代雪梅, 李生刚. 基于哈希技术的BoVW图像检索[J]. 微电子学与计算机, 2016, 33(12): 11-14+19. DOI：

DAI Xue-mei, LI Sheng-gang. Image Retrieval Based on BoVW and Hashing[J]. 2016, 33(12): 11-14. DOI：

摘要

本文提出了一种基于哈希技术的BoVW图像检索方法.选择合适的生成二进制哈希码的哈希算法

将局部特征点保持相似性地映射为二进制哈希码.再在二进制哈希码上进行k-means

生成视觉词为二进制码的视觉词典.最后

用视觉单词的词频向量表示图像内容

并根据词频向量对图像进行检索.实验结果表明

该方法可以缩短视觉词典生成的时间

占用更少的存储空间.

Abstract

Currently

this paper propose an image retrieval method based on BoVW and hashing.Firstly

select an appropriate binary hashing algorithm

which map the similar local feature points into similar binary hash codes.Second

play k-means on the binary hash codes and generate the visual vocabulary with binary visual word.Finally

the image is represented with the word frequency vector of visual words

and image retrieval is achieved based on the word frequency vector.Experimental results on SIFT-1Mand Caltech-256 data sets show that the proposed method can reduce the visual vocabulary generation time and take up less storage space.

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references

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Datar M, Immorlica N, Indyk P, et al. Locality sensitive hashing scheme based on p-stable distributions[C]//Proceedings of the ACM Symposium on Computational Geometry. New York, USA, 2004.

Gong Y, Lazebnik S, Gordo A, et al. Iterative quantization: A Procrustean approach to learning binary codes for large-scale image retrieval[J]. IEEE Transactions on Software Engineering, 2013, 35(12): 2916-2945. DOI: 2916

Mu Y D, Sun U, Han T X. Randomized locality sensitive vocabularies for bag-of-features model[C]//Proceedings of the 11th European conference on computer vision conference on Computer vision. Germany, Berlin, Heidelberg, 2010.

Li F F, Fergus R, Perona P. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101object categories[J]. Computer Vision and Image Understanding, 2007, 106(1): 59-70. DOI: 59

Lowe D. Distinctive image features from scale-invariant keypoints[J]. Int. J. Comput. Vis., 2004, 60(2): 91-110. DOI: 91

Lin W, Wang J, Ji R, et al. Supervised hashing with kernels[C]//Proc. CVPR 2012. USA, Rhode Island, Providence, 2012.

Gong Y, Pavlowski M, Yang Fei. Web scale photo hash clustering on a single machine[C]//Proc. CVPR2015. USA, Boston, 2015.

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