Visual Comparison of Images Using Multiple Kernel Learning for Ranking

Amr Sharaf, Mohamed E. Hussein and Mohamed A. Ismail

Abstract

Ranking is the central problem for many applications such as web search, recommendation systems, and visual comparison of images. In this paper, the multiple kernel learning framework is generalized for the learning to rank problem. This approach extends the existing learning to rank algorithms by considering multiple kernel learning and consequently improves their effectiveness. The proposed approach provides the convenience of fusing different features for describing the underlying data. As an application to our approach, the problem of visual image comparison is studied. Several visual features are used for describing the images and multiple kernel learning is adopted to find an optimal feature fusion. Experimental results on three challenging datasets show that our approach outperforms the state-of-the art and is significantly more efficient in runtime.

Session

Learning and Recognition

Files

PDF iconExtended Abstract (PDF, 1505K)
PDF iconPaper (PDF, 1673K)

DOI

10.5244/C.29.95
https://dx.doi.org/10.5244/C.29.95

Citation

Amr Sharaf, Mohamed E. Hussein and Mohamed A. Ismail. Visual Comparison of Images Using Multiple Kernel Learning for Ranking. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 95.1-95.13. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_95,
	title={Visual Comparison of Images Using Multiple Kernel Learning for Ranking},
	author={Amr Sharaf and Mohamed E. Hussein and Mohamed A. Ismail},
	year={2015},
	month={September},
	pages={95.1-95.13},
	articleno={95},
	numpages={13},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.95},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.95}
}