计算机科学 ›› 2016, Vol. 43 ›› Issue (7): 83-88.doi: 10.11896/j.issn.1002-137X.2016.07.014
• 2015年第二十四届全国多媒体学术会议 • 上一篇 下一篇
张旭,蒋建国,洪日昌,杜跃
ZHANG Xu, JIANG Jian-guo, HONG Ri-chang and DU Yue
摘要: 目前,大部分图像分类算法为了获取较高的性能均需要充分的训练学习过程,然而在实际应用中,往往存在训练样本不足及过拟合等问题。为了避免上述问题出现,在朴素贝叶斯最近邻分类算法的原理框架下,基于非负稀疏编码、低秩稀疏分解以及协作表示提出一种非参数学习的图像分类算法。首先,基于非负稀疏编码和最大值汇聚操作表示图像信息,并构建具有低秩性质的同类训练图像集的局部特征矩阵;其次,采用低秩稀疏分解结合别类标签信息构建两类视觉词典以充分利用同类图像的相关性和差异性;最后基于协作表示表征测试图像并进行分类决策,实验结果验证了所提算法的有效性。
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