基于SVM的雾天图像分类技术研究

针对自适应去雾系统对雾气浓度自动识别的需求, 提出了一种基于SVM和混合特征的雾天图像分类算法。结合雾天图像特点, 选用暗通道特征、小波特征以及去均值归一化特征组成混合特征向量, 用于描述不同雾气浓度下图像的特征差异。通过SVM算法对混合特征向量进行监督学习, 最终实现雾天图像的自动识别与分类。实验结果表明, 算法能够有效地识别与区分无雾、轻雾、浓雾图像, 为去雾系统自适应地根据雾气浓度选取去雾参数提供了良好的分类参考。

Abstract

Aiming at the need for automatic identification of haze concentration in the adaptive dehazing system, this paper presents an algorithm for haze image classification based on Support Vector Machine (SVM) and mixed feature. On the basis of the characteristics of the haze image, the mixed feature vector composed of the dark channel feature, the wavelet feature and the Mean Subtracted Contrast Normalized (MSCN) feature is adopted to describe the characteristic differences of the images with different haze concentration. The SVM classifier implements the automatic identification and classification of haze images through supervised learning of the mixed feature vectors. Experimental results show that the method can effectively identify the images of haze-free, thin haze and dense haze, which provides a good basis for the dehazing system to select dehazing parameters adaptively based on the haze concentration.

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李可, 陈洪亮, 张生伟, 万锦锦. 基于SVM的雾天图像分类技术研究[J]. 电光与控制, 2018, 25(3): 37. LI Ke, CHENG Hongliang, ZAHNG Shengwei, WAN Mianmian. An SVM Based Technology for Haze Image Classification[J]. Electronics Optics & Control, 2018, 25(3): 37.

基于SVM的雾天图像分类技术研究

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