The New Descriptor Algorithm of Local Aggregation Features Based on SIFT and MSE

HE Lin-yuan; BI Du-yan; MA Shi-ping; ZHOU Li; NAN Dong

doi:10.3969/j.issn.0372-2112.2014.08.025

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The New Descriptor Algorithm of Local Aggregation Features Based on SIFT and MSE

更新时间：2025-07-16

- The New Descriptor Algorithm of Local Aggregation Features Based on SIFT and MSE
- Acta Electronica Sinica Vol. 42, Issue 8, Pages: 1619-1623(2014)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,陕西,西安,710049
  2. 空军工程大学航空航天工程学院,陕西,西安,710038
  3. 西安交通大学电子与信息工程学院,陕西,西安,710049
  4. 空军工程大学航空航天工程学院,陕西,西安,710038
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61175029, No.61203268, No.61202339)
- DOI：10.3969/j.issn.0372-2112.2014.08.025
  CLC： TP391.4
- Published：2014
- 稿件说明：
移动端阅览
HE Lin-yuan, BI Du-yan, MA Shi-ping, et al. The New Descriptor Algorithm of Local Aggregation Features Based on SIFT and MSE[J]. Acta Electronica Sinica, 2014, 42(8): 1619-1623.
DOI：

HE Lin-yuan, BI Du-yan, MA Shi-ping, et al. The New Descriptor Algorithm of Local Aggregation Features Based on SIFT and MSE[J]. Acta Electronica Sinica, 2014, 42(8): 1619-1623. DOI： 10.3969/j.issn.0372-2112.2014.08.025.

摘要

为寻找更具鲁棒性和计算简便的特征描述子，提出了一种基于SIFT和MSE的局部聚集特征描述算法.分析说明了该方法在继承SIFT算法良好性质的基础上，通过对多尺度下信息熵的估计，能够快速准确找出图像局部结构特征并利用改进的非线性降维方法对特征描述子进行特征重划.实验结果表明，在图像尺度缩放、旋转、模糊、亮度变化等多种变换条件下，该描述子不仅能够取得更多的特征效果，并且计算速度较原算法大幅提升.该算法适用于实时性要求较高，存在旋转、尺度缩放、亮度差异等变换下的结构图像寻找描述子.

Abstract

In order search a more robustness and convenient count method which shows a new feature descriptor algorithm is proposed in this paper.It analyzes and explains this way could quickly and accurately to describe local structure features based on inherit a higher quality of SIFT and MSE.In addition

it makes use of a changed locally linear embedding approach to process data so that it could reduce dimension.Experiment has drawn the conclusion that not only it could obtain more and betters the proposed descriptor but also the count speed could faster than SIFT for the image with zoom

rotation

blurring and illumination varying.This algorithm is suitable for searching the images which has structured features

when it exits multiple of varying.

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