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A SAMPLE RECOGNITION PROBLEM Joseph Tighe University of North Carolina at Chapel Hill.

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Presentation on theme: "A SAMPLE RECOGNITION PROBLEM Joseph Tighe University of North Carolina at Chapel Hill."— Presentation transcript:

1 A SAMPLE RECOGNITION PROBLEM Joseph Tighe University of North Carolina at Chapel Hill

2 What is recognition?

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5 He et al. (2004), Hoiem et al. (2005), Shotton et al. (2006, 2008, 2009), Verbeek and Triggs (2007), Rabinovich et al. (2007), Galleguillos et al. (2008), Gould et al. (2009), etc. Figure from Shotton et al. (2009)

6 What is recognition?  Have some entity  photo  bounding box in photo  pixel  Assign semantic meaning  scene type, tags, sentence  object label, action  semantic class, material, geometric orientation

7 Our Problem  Entity: Images  Goal: Assign 1 of 4 labels (airplane, car, face, motorbike

8 Finding Similar Images

9 Ocean Open Field Highway Street Forest Mountain Inner City Tall Building What is depicted in this image? Which image is most similar? Then assign the label from the most similar image

10 Pixels are a bad measure of similarity Most similar according to pixel distanceMost similar according to “Bag of Words”

11 Origin of the Bag of Words model  Orderless document representation:  frequencies of words from a dictionary Salton & McGill (1983) US Presidential Speeches Tag Cloud http://chir.ag/phernalia/preztags/

12 What are words for an image?

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17 Wing Tail WheelBuildingPropeller

18 Wing Tail WheelBuilding PropellerJet Engine

19 Wing Tail WheelBuilding PropellerJet Engine

20 Wing Tail WheelBuilding PropellerJet Engine

21 But where do the words come from?

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24 Then where does the dictionary come from?

25 Example Dictionary Source: B. Leibe

26 Another dictionary … … … … Source: B. Leibe

27 Fei-Fei et al. 2005

28 Outline of the Bag of Words method  Divide the image into patches  Assign a “word” for each patch  Count the number of occurrences of each “word” in the image

29 Matlab Demo


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