Optimal Linear Representations of Images for Object Recognition

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.

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Xiuwen Liu, Anuj Srivastava, Kyle Gallivan
• Keywords: Appearance-Based Object Recognition, Computer Vision, CVPR 2003, Optimal Linear Representations, Recognition Performance Function, Stochastic Gradient Algorithm