Learning to Rank Based on Subsequences
Basura Fernando, Efstratios Gavves, Damien Muselet, Tinne Tuytelaars
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.
BibTeX
@inproceedings{iccv2015_learningtorankba,
title = {Learning to Rank Based on Subsequences},
author = {Basura Fernando and Efstratios Gavves and Damien Muselet and Tinne Tuytelaars},
booktitle = {ICCV 2015},
year = {2015}
}