基于Fisher向量的材料分类分量内相关性研究
Probing the Intra-Component Correlations within Fisher Vector for Material Classification
摘要：
Fisher矢量（FV）已成为一种流行的图像表示方法。FV框架的一个值得注意的基本假设是，局部描述符在每个聚类内都是去相关的，因此每个高斯的协方差矩阵可以简化为对角的。虽然FV通常依赖于主成分分析（PCA）来解相关局部特征，但是PCA被应用于整个训练数据，并且因此其仅对角化\textit{universal}协方差矩阵，而不是对角化那些关于.本地组件。因此，局部去相关假设在实践中通常不被支持。为了放宽这一假设，本文提出了一个完整的模型的Fisher向量，这是被称为完成的Fisher向量（CFV）。CFV是FV的一个更一般的框架，因为它不仅编码方差，而且编码白化局部描述符的相关性。因此，CFV导致改进的辨别能力。我们以材料分类任务为例，实验表明：1）CFV在所有参数设置下都优于FV; 2）CFV对混合物中组分数量的变化具有鲁棒性; 3）即使在相对较小的视觉词汇量下，CFV仍然在两个具有挑战性的数据集上工作得很好。
英文摘要：
Fisher vector (FV) has become a popular image representation. One notable underlying assumption of the FV framework is that local descriptors are well decorrelated within each cluster so that the covariance matrix for each Gaussian can be simplified to be diagonal. Though the FV usually relies on the Principal Component Analysis (PCA) to decorrelate local features, the PCA is applied to the entire training data and hence it only diagonalizes the \textit{universal} covariance matrix, rather than those w.r.t. the local components. As a result, the local decorrelation assumption is usually not supported in practice. To relax this assumption, this paper proposes a completed model of the Fisher vector, which is termed as the Completed Fisher vector (CFV). The CFV is a more general framework of the FV, since it encodes not only the variances but also the correlations of the whitened local descriptors. The CFV thus leads to improved discriminative power. We take the task of material categorization as an example and experimentally show that: 1) the CFV outperforms the FV under all parameter settings; 2) the CFV is robust to the changes in the number of components in the mixture; 3) even with a relatively small visual vocabulary the CFV still works well on two challenging datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
