Multiple Kernels for Object Detection

Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image subwindows. We use multiple kernel learning of Varma and Ray (ICCV 2007) to learn an optimal combination of exponential 2 kernels, each of which captures a different feature channel. Our features include the distribution of edges, dense and sparse visual words, and feature descriptors at different levels of spatial organization. Such a powerful classifier cannot be tested on all image sub-windows in a reasonable amount of time. Thus we propose a novel three-stage classifier, which combines linear, quasi-linear, and non-linear kernel SVMs. We show that increasing the non-linearity of the kernels increases their discriminative power, at the cost of an increased computational complexity. Our contributions include (i) showing that a linear classifier can be evaluated with a complexity proportional to the num...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Andrea Vedaldi, Varun Gulshan, Manik Varma, Andrew Zisserman
• Keywords: Computer Vision, ICCV 2009, Multiple Kernels, Object Detection