Wavelet Frame Accelerated Reduced Support Vector Machines
Wavelet Frame Accelerated Reduced Support Vector Machines
复制标题
小波框架加速简化支持向量机
DOI：
10.1109/tip.2008.2001393
复制
发表时间：
2008
影响因子：
10.6
通讯作者：
T. Vetter
中科院分区：
文献类型：
--
作者：
Matthias Rätsch;G. Teschke;S. Romdhani;T. Vetter
提出了一种降低支持向量机分类器运行时间复杂度的新方法。新的训练算法快速、简单。这是通过过完备的小波变换来实现的，该变换寻找支持向量的最佳逼近。结果表明，小波理论为支持向量机的决策函数与分类器之间的距离提供了一个上界。由于使用了支持向量的Haar小波近似，从而实现了高效的基于图像的积分核评估，因此所获得的分类器是快速的。这为早期拒绝易于区分的向量提供了一组日益复杂的级联分类器。这种优秀的运行时性能是通过使用针对合并的数量的分级评估以及针对简化的集合向量的近似精度的附加评估来实现的。本文将该算法应用于人脸检测问题，但也可用于其他基于图像的分类。与支持向量机相比，该算法具有530倍的加速比，使标准PC上的人脸检测速度超过25fps。
In this paper, a novel method for reducing the runtime complexity of a support vector machine classifier is presented. The new training algorithm is fast and simple. This is achieved by an over-complete wavelet transform that finds the optimal approximation of the support vectors. The presented derivation shows that the wavelet theory provides an upper bound on the distance between the decision function of the support vector machine and our classifier. The obtained classifier is fast, since a Haar wavelet approximation of the support vectors is used, enabling efficient integral image-based kernel evaluations. This provides a set of cascaded classifiers of increasing complexity for an early rejection of vectors easy to discriminate. This excellent runtime performance is achieved by using a hierarchical evaluation over the number of incorporated and additional over the approximation accuracy of the reduced set vectors. Here, this algorithm is applied to the problem of face detection, but it can also be used for other image-based classifications. The algorithm presented, provides a 530-fold speedup over the support vector machine, enabling face detection at more than 25 fps on a standard PC.