Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
IEEE Trans Image Process. 2013 Feb;22(2):631-43. doi: 10.1109/TIP.2012.2220151. Epub 2012 Sep 21.
Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large scene. In this paper, we introduce a new classifier called Maximum Margin Correlation Filter (MMCF), which, while exhibiting the good generalization capabilities of SVM classifiers, is also capable of localizing objects of interest, thereby avoiding the need for image centering as is usually required in SVM classifiers. In other words, MMCF can simultaneously localize and classify objects of interest. We test the efficacy of the proposed classifier on three different tasks: vehicle recognition, eye localization, and face classification. We demonstrate that MMCF outperforms SVM classifiers as well as well known correlation filters.
支持向量机 (SVM) 分类器在许多计算机视觉任务中都很受欢迎。在大多数情况下，SVM 分类器假设要分类的对象位于查询图像的中心，但这并不总是有效的，例如，在大场景中定位和分类特定类别的车辆时。在本文中，我们引入了一种称为最大间隔相关滤波器 (MMCF) 的新分类器，它在表现出 SVM 分类器的良好泛化能力的同时，还能够定位感兴趣的对象，从而避免了通常在 SVM 分类器中需要的图像中心定位。换句话说，MMCF 可以同时定位和分类感兴趣的对象。我们在三个不同的任务上测试了所提出的分类器的有效性：车辆识别、眼睛定位和面部分类。我们证明 MMCF 优于 SVM 分类器以及著名的相关滤波器。
