This document summarizes a tutorial on large-scale visual recognition. The tutorial aims to provide tools for handling large datasets, including scalable image representations like VLAD and Fisher Vectors, and efficient matching and learning techniques. It also shows how large-scale retrieval and classification are converging, with retrieval becoming more machine learning-based and classification more cost-aware. Finally, it demonstrates that large-scale visual recognition does not require huge resources, with examples of searching 100 million images in 250ms and training the 2010 ImageNet challenge in days on a single server.

1. Large-scale visual recognition
Conclusion
Florent Perronnin, XRCE
Hervé Jégou, INRIA
CVPR tutorial
June 16, 2012

2. 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

3. 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

4. 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