http://people.csail.mit.edu/torralba/shortCourseRLOC/
| 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
- Part 2: Multiple object categories
- Part 4: Summary and datasets (.pptx)
Slides CVPR 2007
- Introduction (.ppt)
- Part 1: Bag of words models (.ppt)
- Part 2: Part based models (.ppt)
- Part 3: Discriminative models (.ppt)
- Part 4: Combined segmentation and recognition (.ppt)
- Summary and datasets (.ppt)
Slides ICCV 2005
- Overview (.ppt)
- Introduction (.ppt)
- Part 1: Bag of words models (.ppt)
- Part 2: Part based models (.ppt)
- Part 3: Discriminative models (.ppt)
- Part 4: Combined segmentation and recognition (.ppt), movie (.mpg)
- Summary and datasets (.ppt)
- References (.ppt)
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 |
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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.
