Carnegie Mellon University, Pittsburgh, PA 15213, USA.
IEEE Trans Pattern Anal Mach Intell. 2010 Jul;32(7):1335-41. doi: 10.1109/TPAMI.2010.75.
Linear filters are ubiquitously used as a preprocessing step for many classification tasks in computer vision. In particular, applying Gabor filters followed by a classification stage, such as a support vector machine (SVM), is now common practice in computer vision applications like face identity and expression recognition. A fundamental problem occurs, however, with respect to the high dimensionality of the concatenated Gabor filter responses in terms of memory requirements and computational efficiency during training and testing. In this paper, we demonstrate how the preprocessing step of applying a bank of linear filters can be reinterpreted as manipulating the type of margin being maximized within the linear SVM. This new interpretation leads to sizable memory and computational advantages with respect to existing approaches. The reinterpreted formulation turns out to be independent of the number of filters, thereby allowing the examination of the feature spaces derived from arbitrarily large number of linear filters, a hitherto untestable prospect. Further, this new interpretation of filter banks gives new insights, other than the often cited biological motivations, into why the preprocessing of images with filter banks, like Gabor filters, improves classification performance.
线性滤波器被广泛用作计算机视觉中许多分类任务的预处理步骤。特别是，在计算机视觉应用中，如人脸识别和表情识别，现在通常采用应用 Gabor 滤波器后再进行分类阶段，如支持向量机（SVM）。然而，在训练和测试期间，由于连接的 Gabor 滤波器响应的高维性，会出现内存需求和计算效率方面的基本问题。在本文中，我们展示了如何重新解释应用滤波器组的预处理步骤，即将最大化线性 SVM 中的边距类型。这种新的解释相对于现有方法具有更大的内存和计算优势。重新解释的公式与滤波器的数量无关，从而允许检查来自任意数量的线性滤波器的特征空间，这是以前无法测试的前景。此外，这种对滤波器组的新解释除了经常引用的生物学动机之外，还提供了有关为什么用滤波器组（如 Gabor 滤波器）预处理图像可以提高分类性能的新见解。
