CSCI598A: Robot Intelligence
Jan. 27, 2015
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Pros/Cons of Global Representations
Global representations
• Can provide global information (e.g., object poses).
• Are NOT invariant to object orientations, image
distortions, etc.
• Typically need pre-processing steps to locate the
objects (e.g., sliding window).
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Bag-of-words Representation
• What is in the picture?
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Bag-of-words Representation
• What is in the picture?
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Bag-of-words Representation
• What is in the picture?
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Object
Bag of ‘words’
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Example 1: Bag-of-words models
• Orderless document representation: frequencies of words from a dictionary
Salton & McGill (1983)
US Presidential Speeches Tag Cloud
http://chir.ag/phernalia/preztags/
Example 1: Bag-of-words models
• Orderless document representation: frequencies of words from a dictionary
Salton & McGill (1983)
US Presidential Speeches Tag Cloud
http://chir.ag/phernalia/preztags/
Example 1: Bag-of-words models
• Orderless document representation: frequencies of words from a dictionary
Salton & McGill (1983)
US Presidential Speeches Tag Cloud
http://chir.ag/phernalia/preztags/
Example 1: Bag-of-words models
• Orderless document representation: frequencies of words from a dictionary
Salton & McGill (1983)
Examp2: Texture recognition
• Texture is characterized by the repetition of basic elements or textons
• For stochastic textures, it is the identity of the textons, not their spatial
arrangement, that matters
Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001;
Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003
Example 2: Texture recognition
histogram
Universal texton dictionary
•
Works pretty well for image-level classification
Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001;
Schmid
2001;
Varma
& Zisserman,
2002,
2003;
Lazebnik,
Ponce,
2003
Csurka
et al. (2004),
Willamowski
et al. (2005),
Grauman
& Darrell
(2005),Schmid
Sivic et al.&(2003,
2005)
Example 3: Bags of features for scene recognition
face, flowers, building
•
Works pretty well for image-level classification
Csurka et al. (2004), Willamowski et al. (2005), Grauman & Darrell (2005), Sivic et al. (2003, 2005)
Bag of features (BoF): outline
1. Feature detection
2. Feature description
Bag of features (BoF): outline
1. Feature detection
2. Feature description
Bag of features (BoF): outline
1. Feature detection
2. Feature description
3. Dictionary learning
Codeword
(
,
,
... )
Codeword
(
,
,
... )
Codeword
(
,
,
... )
Bag of features (BoF): outline
1. Feature detection
2. Feature description
3. Dictionary learning
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Bag of features (BoF): outline
1. Feature detection
2. Feature description
3. Dictionary learning
Bag of features (BoF): outline
1.
2.
3.
4.
Feature detection
Feature description
Dictionary learning
Bag-of-features representation