Skip to main content

Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Abstract

Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL) methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL) approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Publisher note

To access the full article, please see PDF.

Author information

Correspondence to Yonghong Tian.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Yang, J., Li, Y., Tian, Y. et al. Per-Sample Multiple Kernel Approach for Visual Concept Learning. J Image Video Proc 2010, 461450 (2010). https://doi.org/10.1155/2010/461450

Download citation

Keywords

  • Computer Vision
  • Kernel Weight
  • Concept Learn
  • Publisher Note
  • Multiple Kernel Learning