南京大学学报(自然科学版) ›› 2020, Vol. 56 ›› Issue (1): 5156.doi: 10.13232/j.cnki.jnju.2020.01.006
Zilong Li1,2,3(),Yong Zhou2,Rong Bao1
摘要:
近年来,距离度量学习已经成为图像分类领域的研究热点之一,图像到类距离的度量作为其中的一种方法,取得了不错的分类效果.该方法是一种非参数方法,但由于缺少训练学习,其分类性能很容易受干扰因素的影响,为此提出一种基于AdaBoost算法的图像到类距离学习的图像分类方法.首先将图像到类的距离进行阈值化处理,并使用线性分段函数作为图像到类距离的评价函数,然后将该评价函数作为弱分类器加入到AdaBoost算法中生成一个强分类器.为了选择最优的弱分类器,使用粒子群优化算法确定图像的相似性阈值,再基于权重错误误差最小化原则得到距离评价函数的两个评价值.最后通过实验验证,该方法在Scene?15和Caltech?101图像数据集上比其他方法有更好的分类效果.
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