| 基于多特征融合的红外图像分类研究 |
| 投稿时间：2022-07-08 修订日期：2022-07-14 点此下载全文 |
| 引用本文：于晓,李朝.基于多特征融合的红外图像分类研究[J].红外,2022,43(10):32~42 |
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| 基金项目:天津市教委科研计划项目(2018KJ133) |
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| 中文摘要:针对传统红外图像目标分类方法准确率低的问题，提出了一种用结合多特征融合的粒子群优化(Particle Swarm Optimization, PSO)算法来优化支持向量机(Support Vector Machine, SVM)的方法。该方法采用方向梯度直方图(Histogram of Oriented Gradient, HOG)和局部二值模式(Local Binary Pattern, LBP)两类特征描述红外图像中目标的轮廓特征和局部纹理，从不同的方面展现红外图像的特点，在图像的特征表达上具有一定的互补性。在特征提取后对样本数据进行凸包算法计算，得到一些具有代表性的样本数据，从而提高分类计算效率；在分类模型训练时，采用PSO算法优化SVM，寻找SVM的最优惩罚因子和核参数，从而提高分类模型的准确率。实验结果表明，多特征融合的分类模型的准确率比单一特征的分类模型提高近10%，且经PSO优化的SVM最终模型的分类准确率高达99%。 |
| 中文关键词:多特征融合 支持向量机 粒子群优化算法 特征提取 红外图像分类 |
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| Research on Infrared Image Classification Based on Multi-Feature Fusion |
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| Abstract:Aiming at the problem of low accuracy of traditional infrared image target classification methods, a method of SVM based on PSO combined with multi-feature fusion is proposed. HOG and LBP are used to describe the contour features and local textures of targets in infrared images. The method shows the characteristics of infrared image from different aspects, so there is a certain complementarity in the expression of image features. After feature extraction, the convex hull algorithm is used to calculate the sample data, and some representative sample data are obtained, so as to improve the efficiency of classification calculation. In the training of classification model, PSO is used to optimize SVM to find the optimal penalty factor and kernel parameters of SVM, so as to improve the accuracy of classification model. The experimental results show that the accuracy of the multi-feature fusion classification model is nearly 10% higher than that of the single-feature classification model, and the classification accuracy of the final SVM model optimized by PSO is as high as 99%. |
| keywords:multi-feature fusion support vector machine particle swarm optimization feature extraction infrared image classification |
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