- Research Article
- Open Access
Per-Sample Multiple Kernel Approach for Visual Concept Learning
EURASIP Journal on Image and Video Processing volume 2010, Article number: 461450 (2010)
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.
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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
- Computer Vision
- Kernel Weight
- Concept Learn
- Publisher Note
- Multiple Kernel Learning