Visual Comparison of Images Using Multiple Kernel Learning for Ranking

Visual Comparison of Images Using Multiple Kernel Learning for Ranking. Sharaf, A., Hussein, M. E., & Ismail, M. A. In Procedings of the British Machine Vision Conference 2015, pages 95.1–95.13, 2015. British Machine Vision Association.

Paper doi abstract bibtex

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.

@inproceedings{sharaf_visual_2015,
	location = {Swansea},
	title = {Visual Comparison of Images Using Multiple Kernel Learning for Ranking},
	rights = {All rights reserved},
	isbn = {978-1-901725-53-7},
	url = {http://www.bmva.org/bmvc/2015/papers/paper095/index.html},
	doi = {10.5244/C.29.95},
	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.},
	eventtitle = {British Machine Vision Conference 2015},
	pages = {95.1--95.13},
	booktitle = {Procedings of the British Machine Vision Conference 2015},
	publisher = {British Machine Vision Association},
	author = {Sharaf, Amr and Hussein, Mohamed E. and Ismail, Mohamed A.},
	urldate = {2019-05-01},
	year = {2015},
	langid = {english},
	file = {Sharaf et al. - 2015 - Visual Comparison of Images Using Multiple Kernel .pdf:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\T7KPQZHA\\Sharaf et al. - 2015 - Visual Comparison of Images Using Multiple Kernel .pdf:application/pdf}
}

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