用字典学习识别几何形状，效果优于传统方法。
Recognition of Geometrical Shapes by Dictionary Learning
- 构建过完备字典，用少量原子表示形状特征
- 优化方法选择显著影响识别准确率
- 适合图像识别与特征提取初学者参考
字典学习是一种生成过完备向量集（称为原子）的通用方法，可仅用少数原子表示输入数据。以往研究主要利用其强大的表达能力，用于图像重建等任务。本文首次将字典学习应用于几何形状识别，验证了优化方法的选择对识别性能有显著影响。实验结果表明，该方法在形状识别任务中具有潜力，可作为有效替代方案。
原文摘要 · Abstract (English)
Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks.
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