Region Covariance: A Fast Descriptor for Detection and Classification
Region Covariance: A Fast Descriptor for Detection and Classification
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DOI：
10.1007/11744047_45
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
Oncel Tuzel;F. Porikli;P. Meer
中科院分区：
文献类型：
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作者：
Oncel Tuzel;F. Porikli;P. Meer
我们描述了一个新的区域描述符，并将其应用到两个问题，目标检测和纹理分类。d特征的协方差，例如，三维颜色向量、强度关于toxandy的一阶和二阶导数的范数等，表征感兴趣的区域。我们描述了一种基于积分图像的快速协方差计算方法。这里提出的想法是更一般的图像总和或直方图，这是以前已经公布的，并与一系列的整体图像的协方差是通过一些算术运算获得。协方差矩阵不存在于欧氏空间中，因此我们使用了一个包含广义特征值的距离度量，它也是从正定矩阵的李群结构得出的。特征匹配是在距离度量下的简单的最近邻搜索，并且使用积分图像非常快速地执行。如图所示，协方差特征的性能上级于其他方法，并且协方差矩阵还吸收了大的旋转和照明变化。
We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance ofd-features, e.g., the three-dimensional color vector, the norm of first and second derivatives of intensity with respect toxandy, etc., characterizes a region of interest. We describe a fast method for computation of covariances based onintegral images. The idea presented here is more general than the image sums or histograms, which were already published before, and with a series of integral images the covariances are obtained by a few arithmetic operations. Covariance matrices do not lie on Euclidean space, therefore we use a distance metric involving generalized eigenvalues which also follows from the Lie group structure of positive definite matrices. Feature matching is a simple nearest neighbor search under the distance metric and performed extremely rapidly using the integral images. The performance of the covariance features is superior to other methods, as it is shown, and large rotations and illumination changes are also absorbed by the covariance matrix.