计算机科学 ›› 2014, Vol. 41 ›› Issue (5): 288-291.doi: 10.11896/j.issn.1002-137X.2014.05.061
林武旭,成科扬,张建明
LIN Wu-xu,CHENG Ke-yang and ZHANG Jian-ming
摘要： 图像中所蕴含的属性对于图像识别有着重要作用,以往的传统分类方法往往忽略了这些特性,为此,提出一种将稀疏表示和属性学习结合用于图像分类的新方法。该方法首先对图像特征进行稀疏分解,利用系数稀疏表示重构图像特征,然后将重构的特征数据用于属性学习,通过属性分类器的训练学习完成对目标图像的属性识别,达到识别出图像种类的目的。在植物数据集上的对比试验证实了该算法的有效性和在识别准确率上相对于传统识别算法的提升。
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