Vedaldi, A. ; Gulshan, V. ; Varma, M. ; Zisserman, A. (2009) Multiple Kernels for Object Detection In: Proceedings of the International Conference on Computer Vision.
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Abstract
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 χ 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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Institute of International Conference on Computer Vision. |
| ID Code: | 119699 |
| Deposited On: | 16 Jun 2021 09:35 |
| Last Modified: | 16 Jun 2021 09:35 |
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