计算机科学 ›› 2015, Vol. 42 ›› Issue (3): 301-306.doi: 10.11896/j.issn.1002-137X.2015.03.062
仝伯兵,王士同
TONG Bo-bing and WANG Shi-tong
摘要： 为提高视频烟雾检测的准确性,提出一种基于概率的两层最近邻自适应度量分类算法(PTLNN)来进行烟雾检测。该算法以最小化平均绝对误差为原则,结合AdaBoost和KNN算法的优势,充分考虑局部和全局的样本分布,能明显提升分类精度。采用离散余弦变换(DCT)和离散小波变换(DWT)两种方式对烟雾特征进行提取,并验证算法性能。通过与传统算法的对比实验发现,采用离散余弦变换并结合PTLNN算法在视频烟雾检测方面具有更好的效果,既满足实时性要求又提高了检测精度。
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