Object recognition based on spatial Active Basis template
This article presents a method for the object classification that combines a generative template and a discriminative classifier. The method is a variant of the support vector machine (SVM), which uses Multiple Kernel Learning (MKL). The features are extracted from a generative template so called Active Basis template. Before using them for object classification, we construct a visual vocabulary by clustering a set of training features according to their orientations. To keep the spatial information, a “spatial pyramid is used. The strength of this approach is that it combines the rich information encoded in the generative template, the Active Basis, with the discriminative power of the SVM algorithm. We show promising results of experiments for images from the LHI dataset.
Object Recognition Deformable Templates MKL
Shaowu Peng Jingcheng Xu
School of Software Engineering, South China University of Technology, Guangzhou, China
国际会议
桂林
英文
1-6
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）