结合VLAD特征和稀疏表示的图像检索

颜文; 金炜; 符冉迪

doi:10.11959/j.issn.1000-0801.2016308

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结合VLAD特征和稀疏表示的图像检索

研究与开发 | 更新时间：2024-06-05

- 结合VLAD特征和稀疏表示的图像检索
- Image retrieval based on the feature of VLAD and sparse representation
- 电信科学 2016年32卷第12期页码：80-85
- 作者机构：
- 作者简介：
  
  [ "颜文（1990-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为图像处理、模式识别。" ]
  [ "金炜（1969-），男，博士，宁波大学信息科学与工程学院副教授，主要研究方向为数字图像处理。" ]
  [ "符冉迪（1971-），男，宁波大学信息科学与工程学院副教授，主要从事数字图像处理、模式识别方面的研究工作。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61271399);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY16F010001);宁波市自然科学基金资助项目;The Natural Science Foundation of Ningbo of China(2016A610091)
- DOI：10.11959/j.issn.1000-0801.2016308
  中图分类号： TP391
- 网络出版日期：2016-12，
  
  纸质出版日期：2016-12-20
- 稿件说明：
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颜文, 金炜, 符冉迪. 结合VLAD特征和稀疏表示的图像检索[J]. 电信科学, 2016,32(12):80-85.

Wen YAN, Wei JIN, Randi FU. Image retrieval based on the feature of VLAD and sparse representation[J]. Telecommunications science, 2016, 32(12): 80-85.
颜文, 金炜, 符冉迪. 结合VLAD特征和稀疏表示的图像检索[J]. 电信科学, 2016,32(12):80-85. DOI： 10.11959/j.issn.1000-0801.2016308.

Wen YAN, Wei JIN, Randi FU. Image retrieval based on the feature of VLAD and sparse representation[J]. Telecommunications science, 2016, 32(12): 80-85. DOI： 10.11959/j.issn.1000-0801.2016308.

摘要

为了实现快速准确的图像检索目标，提出一种结合VLAD（局部聚合描述符）特征和稀疏表示的图像检索方法。首先，根据图像具有结构细节丰富、局部视觉特征差异明显的特点，提取图像的局部旋转不变SURF特征，并采用局部聚合描述符方法，构造具有旋转不变性的图像VLAD特征，然后将VLAD特征与稀疏表示相结合，设计基于稀疏表示的相似性检索度量准则，实现图像的查询检索。实验结果表明，提出方法在查准率（precision）及平均归一化修正检索排序等指标上，均优于其他几种典型方法，并具有较高的计算效率。

Abstract

In order to achieve the goal of fast and accurate image retrieval

an image retrieval method combining VLAD （vector of locally aggregated descriptor）feature and sparse representation was proposed. Firstly

according to the characteristics of rich structure details and obvious differences for local visual features in image

the local rotation invariant SURF feature of the image was extracted

and the local VLAD feature of the image with rotation invariance was constructed by the local aggregation descriptor method. Then

the VLAD feature was combined with the sparse representation（SR）to design the similarity retrieval metric based on SR

thus the retrieval of the image could be realized. The experimental results show that

proposed method outperforms the compared methods in terms of precision

average normalize modified retrieval rank（ANMRR）and other indicators

and it also has higher computational efficiency.

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references

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