Recognizing and Learning Object Categories --- 连接放送

http://people.csail.mit.edu/torralba/shortCourseRLOC/

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

• 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

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 
  
  
  

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  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.