@inproceedings{8768f3ff2af34e83803a86ec87c0ca2d,
title = "Learning to rank based on subsequences",
abstract = "We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analysing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequences instead. These sub-sequences contain useful structural ranking information that leads to better learnability during training and better generalization during testing. By exploiting sub-sequences, the proposed MidRank improves ranking accuracy considerably on an extensive array of image ranking applications and datasets.",
author = "Basura Fernando and Efstratios Gavves and Damien Muselet and Tinne Tuytelaars",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",
year = "2015",
month = feb,
day = "17",
doi = "10.1109/ICCV.2015.319",
language = "English",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2785--2793",
booktitle = "2015 International Conference on Computer Vision, ICCV 2015",
address = "United States",
}