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。
Currently, in order to achieve high performance, most image classification methods require adequate training and learning process. However, problems such as scarcity of training samples and overfitting of parameters are often en-countered. To avoid these problems, we presented a non-parameter learning algorithm under the framework of Naive Bayes Nearest-Neighbor （NBNN）, where non-negative sparse coding, low rank and sparse decomposition and collaboration representation are jointly employed. Firstly, non-negative sparse coding combined with max pooling is introduced to represent images, and local feature matrices of similar training image sets with low-rank characteristic are generated. Secondly, two kinds of visual dictionary with category labels are constructed by leveraging low rank and sparse decomposition to make full use of the correlation and diversity of images with the same category label Lastly, test images are represented based on collaboration representation for classification. Experimental results demonstrate effectiveness of the proposed algorithm.
出处
《计算机科学》
CSCD
北大核心
2016年第7期83-88,共6页
Computer Science
基金
国家自然科学基金项目(61172164)
安徽省自然科学基金项目(1508085QF114
1608085QF144)资助
关键词
图像分类
视觉词袋
稀疏编码
低秩稀疏分解
协作表示
Image classification,Bag of visual words, Sparse coding, Low rank and sparse decomposition, Collaborative representation
