Liu Xiuwen, Srivastava Anuj, Gallivan Kyle
Department of Computer Science, Florida State University, Tallahassee, FL 32306, USA.
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):662-6. doi: 10.1109/TPAMI.2004.1273986.
Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm.
虽然线性表示在图像分析中经常被使用，但其性能在特定应用中很少是最优的。本文提出了一种随机梯度算法，用于寻找图像的最优线性表示，以用于基于外观的目标识别。使用最近邻分类器，指定了一个识别性能函数，并寻找使该性能最大化的线性表示。为了在格拉斯曼流形上解决这个优化问题，引入了一种利用内在流的随机梯度算法。给出了几个实验结果来证明该算法。
