计算机工程 ›› 2020, Vol. 46 ›› Issue (7): 243-250,259.doi: 10.19678/j.issn.1000-3428.0055065

• 图形图像处理 • 上一篇    下一篇

基于LRC-SNN的图像高效重建与识别

索静1, 宋林林2, 李强2   

  1. 1. 太原工业学院 电子工程系, 太原 030000;
    2. 太原理工大学 信息与计算机学院, 山西 晋中 030600
  • 收稿日期:2019-05-30 修回日期:2019-09-10 发布日期:2019-10-10
  • 作者简介:索静(1982-),女,讲师、硕士,主研方向为图像识别;宋林林,硕士研究生;李强,讲师、博士。
  • 基金项目:
    国家自然科学基金青年基金(61703298)。

Efficient Image Reconstruction and Recognition Based on LRC-SNN

SUO Jing1, SONG Linlin2, LI Qiang2   

  1. 1. Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan 030000, China;
    2. College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
  • Received:2019-05-30 Revised:2019-09-10 Published:2019-10-10

摘要: 图像集分类算法种类较多,但多数存在运算繁琐、计算成本高和时效性差的问题。为此,提出一种改进的图像重建与识别算法,利用线性回归分类和共享最近邻子空间分类理论进行图像重建和分类,通过将图像下采样建立的高维空间重建为子空间,避免计算复杂度较高的训练过程。利用各个类别的图像集子空间对测试图像进行回归模型估计,根据回归模型重建测试集中的图像,基于重建图像和原始图像间重建误差最小化法,采用加权投票策略对测试集进行估计以确定图像所属的类别。在UCSD/Honda、CMU、ETH-8和YouTube数据集上进行实验,结果表明,在低分辨率采样条件下,与ADNT算法相比,该算法平均分类精度提高3.6%,运算效率提高10倍,其最快响应时间缩短至2.8 ms。

关键词: 图像集分类, LRC-SNN回归模型, 误差最小化, 加权投票策略, 分类精度, 计算速度

Abstract: Most of existing classification methods for image sets are costly,having high computational complexity and poor timeliness.To address the problem,this paper proposes an improved image reconstruction and recognition algorithm.The algorithm uses the Linear Regression Classification(LRC) and Share Nearest Neighbor(SNN) subspace classification theory for image reconstruction and classification.The high-dimensional space built by image subsampling is taken as subspace to avoid the training process with high computational complexity.Then,subspace of different categories of image sets is used to implement regression model estimation for test images.For images in the test set of regression model reconstruction,their categories are determined by using the weighted voting strategy to estimate the test set under the principle that the errors between reconstructed images and original images should be minimized.Experimental results on UCSD/Honda,CMU,ETH-8 and YouTube datasets show that under low-resolution sampling conditions,compared with the ADNT algorithm,the proposed algorithm increases the average classification accuracy by 3.6%,computational efficiency by 10 times,and shortens the fastest response time to 2.8 ms.

Key words: image set classification, LRC-SNN regression model, error minimization, weighted voting strategy, classification accuracy, computational speed

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