摘要
为实现视频监控设备对雾天天气现象的自动识别,提出了基于K均值聚类算法的雾天天气现象自动识别方法。该方法通过分析雾天天气现象对视频图像采集的影响,提取图像饱和度的均值、方差为特征参数,并利用K均值聚类算法对训练图像进行分类,得到不同图像类别的聚类中心,测试阶段计算不同图像与聚类中心的相异度即可完成分类。实验结果表明,该方法简洁高效,易于实现对大规模图像数据的处理,并能实现图像分类后类别的标注,对雾天的识别率高于90%。
To realize the foggy weather automatic recognition with video surveillance equipment, a method of foggy weather automatic recognition based on K-means clustering algorithm is put forward, in which the influence of foggy weather on video image acquisition is analyzed, and the mean value of the image saturability and variance are extracted as the characteristic pa- rameters. The training images are classified by using K-means clustering algorithm to obtain the clustering center of the different image classification. In the test stage, the classification can be completed by calculating the dissimilarity of different images and clustering centers. The experimental results show this method is simple and efficient, and easy to realize large-scale image data processing, and can realize the category labeling after image classification. The recognition accuracy is higher than 90%.
出处
《现代电子技术》
北大核心
2015年第22期80-83,共4页
Modern Electronics Technique
基金
国家自然科学基金(61250006
61002052
61471412)
关键词
雾天
自动识别
K均值聚类算法
图像饱和度
foggy weather
automatic recognition
K-means clustering algorithm
image saturability
