参数化区域协方差：一种将稀疏编码应用于二阶统计量的有效方法
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
摘要：
稀疏表示已成功应用于信号处理、计算机视觉和机器学习等领域。目前有一种趋势是直接在结构数据上学习稀疏模型，例如区域协方差。然而，当与区域协方差组合时，这样的方法通常需要复杂的计算。我们提出了一种方法，通过构造区域协方差矩阵的欧氏空间表示，将结构化稀疏模型学习问题转化为传统的向量化稀疏建模问题。我们的新代表具有多种优势。几个视觉任务的实验证明了竞争力的性能与国家的最先进的方法。
英文摘要：
Sparse representations have been successfully applied to signal processing, computer vision and machine learning. Currently there is a trend to learn sparse models directly on structure data, such as region covariance. However, such methods when combined with region covariance often require complex computation. We present an approach to transform a structured sparse model learning problem to a traditional vectorized sparse modeling problem by constructing a Euclidean space representation for region covariance matrices. Our new representation has multiple advantages. Experiments on several vision tasks demonstrate competitive performance with the state-of-the-art methods.
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