Large-scale visual recognition Conclusion Florent Perronnin, XRCE Hervé Jégou, INRIA

CVPR tutorial June 16, 2012

Goals of this tutorial

Provide tools to handle large-scale datasets: !! image representations: scaling the BOV, including higher order statistics (VLAD, FV) !! scalable matching/learning: compression, approx. search, SGD, explicit embedding

Goals of this tutorial

Provide tools to handle large-scale datasets: !! image representations: scaling the BOV, including higher order statistics (VLAD, FV) !! scalable matching/learning: compression, approx. search, SGD, explicit embedding

Show convergence of large-scale retrieval and classification: !! retrieval: more and more machine learning !! classification: more and more cost aware

Goals of this tutorial

Provide tools to handle large-scale datasets: !! image representations: scaling the BOV, including higher order statistics (VLAD, FV) !! scalable matching/learning: compression, approx. search, SGD, explicit embedding

Show convergence of large-scale retrieval and classification: !! retrieval: more and more machine learning !! classification: more and more cost aware

Show that LSVR does not necessarily require gigantic resources: !! searching in 100M images in 250ms on a single processor !! train from scratch ILSVRC 2010 in a few days on a single server