Recognizing and Learning Object Categories

Recognizing and Learning Object Categories

Awarded the Best Short Course Prize at ICCV 2005

Li Fei-Fei (Stanford), Rob Fergus (NYU), Antonio Torralba (MIT)

This course reviews current methods for object category recognition, dividing them into four main areas: bag of words models; parts and structure models; discriminative methods and combined recognition and segmentation. The emphasis will be on the important general concepts rather than in depth coverage of contemporary papers. The course is accompanied by extensive Matlab demos.

ICCV 2009 Recognizing and Learning Object Categories: Year 2009

Introduction (.pptx, .pdf)
Part 1: Single object classes
- Bag of words models, Part-based models, and Discriminative models (.pptx)
- Detecting single objects in context (.pptx)
- 3D object models (.pptx)
Part 2: Multiple object categories
- Recognizing a large number of object classes (.pptx)
- Recognizing multiple objects in an image. Sharing and context (.pptx)
- Objects and annotations (.pptx)
Part 4: Summary and datasets (.pptx)

Slides CVPR 2007

Slides ICCV 2005

Matlab code

This set of three demos illustrates the concepts behind several approaches for object recognition. The code consists of Matlab scripts (which should run under both Windows and Linux). The code is for teaching/research purposes only.

Bag of words models	A simple parts and structure model	A simple detector with boosting

Datasets
These are pointers to the datasets used in the demos:

Caltech datasets

LabelMe dataset and annotation tool

PASCAL collection

Acknowledgments

This work was partially supported by the National Science Foundation Grant No. 0413232. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.