李珍,张素兰

• 1:太原科技大学计算机科学与技术学院

摘要(Abstract)：

针对字典学习图像分类中类内结构信息利用不充分导致分类效果不佳的问题，提出一种基于松弛标签一致性字典对学习(Slack Label Consistent Dictionary Pair Learning, SLC-DPL)的图像分类算法。首先，利用投影字典对样本数据进行线性投影，使编码表示系数在保持稀疏性的同时，获取更有效的编码特征；其次，引入标签一致性矩阵，将标签信息和每一个字典项相关联，增强类内稀疏表示的相似性；最后，学习一个松弛矩阵将标签一致性进行动态优化，使学习到的表示系数更加灵活，进而提高字典对算法的可判别性。通过在AR数据集、CMU PIE数据集和USPS数据集上进行实验，验证了该算法可有效提高图像分类的精度。

关键词(KeyWords)： 字典对学习;标签一致性;松弛矩阵

Abstract:

Keywords:

基金项目(Foundation): 山西省自然科学基金(202103021224285);; 太原科技大学研究生教育创新项目(SY2022062)

作者(Author): 李珍,张素兰

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