Learning to Rank Based on Subsequences

ICCV 2015 Basura FernandoEfstratios GavvesDamien MuseletTinne Tuytelaars

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... (read more)

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