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Li Fei-Fei, Princeton Rob Fergus, MIT Antonio Torralba, MIT CVPR 2007 Minneapolis, Short Course, June 17 Recognizing and Learning  Object Categories: Year 2007
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
perceptible vision material thing
Plato said… ,[object Object],[object Object],[object Object],[object Object]
Bruegel, 1564
How many object categories are there? ~10,000 to 30,000 Biederman 1987
So what does object recognition involve?
Verification: is that a lamp?
Detection: are there people?
Identification: is that Potala Palace?
Object categorization mountain building tree banner vendor people street lamp
Scene and context categorization ,[object Object],[object Object],[object Object]
Computational photography
Assisted driving Lane detection Pedestrian and car detection ,[object Object],[object Object],[object Object],meters meters Ped Ped Car
Improving online search Query: STREET Organizing photo collections
Challenges 1: view point variation Michelangelo 1475-1564
Challenges 2: illumination slide credit: S. Ullman
Challenges 3: occlusion Magritte, 1957
Challenges 4: scale
Challenges 5: deformation Xu, Beihong 1943
Challenges 6: background clutter Klimt, 1913
History: single object recognition
History: single object recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Challenges 7: intra-class variation
History: early object categorization
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
~10,000 to 30,000
Object categorization:  the statistical viewpoint vs. ,[object Object],posterior ratio likelihood ratio prior ratio
Object categorization:  the statistical viewpoint posterior ratio likelihood ratio prior ratio ,[object Object],[object Object]
Discriminative ,[object Object],Zebra Non-zebra Decision boundary
Generative ,[object Object],Middle  Low High Middle Low
Three main issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Representation ,[object Object]
Representation ,[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],Learning
[object Object],[object Object],Learning
[object Object],[object Object],[object Object],[object Object],Learning Contains a motorbike
[object Object],[object Object],[object Object],[object Object],[object Object],Learning
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Learning
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Learning
[object Object],[object Object],[object Object],Recognition
Hoiem, Efros, Herbert, 2006
OBJECTS ANIMALS INANIMATE PLANTS MAN-MADE NATURAL VERTEBRATE … .. MAMMALS BIRDS GROUSE BOAR TAPIR CAMERA

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