clasificación de imájenes
DESCRIPTION
algoritmo de reconocimiento de imájenesTRANSCRIPT
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Beyond Bags of Features: Spatial Pyramid
Matching for Recognizing Natural Scene Categories
Svetlana Lazebnik Cordelia Schmid Jean Ponce
September 19, 2011
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Motivation
Consider the problem of recognizing the semantic category of
an image.
Classify a photograph as depicting a scene (forest, street,
oce, etc.)
Bag-of- features approach with global geometric
correspondence
Subdividing the image and computing histograms of local
features at increasingly ne resolutions
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Histogram intersection
I
l = I(H
l
X
,H lY
)=Di=1
min
(H
l
X
(i) ,H lY
(i))
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Pyramid match kernel
matches found at level l also includes all the matches found
l + 1 new matches found at level l : I l I l+1 for l = 0, .., L 1penalize matches found in larger cells:
1
2
Ll
k
L(X ,Y ) = I L +L1l=0
1
2
Ll(I
l I l+1)k
L(X ,Y ) = 12
L
I
0 +Ll=1
1
2
Ll+1 Il
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Pyramid match kernel
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Spatial Matching Scheme
m: feature type
X
m
: coordinates of features of type m
for L levels and M channels
K
L(X ,Y ) =Mm=1k
L(Xm
,Ym
)
Vector dimensionality: M
Ll=0
4
l = M 13
(4
L+1 1)However, these operations are ecient because the histogram
vectors are extremely sparse
The computational complexity of the kernel is linear in the
number of features
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Mercer's kernel
According to Mercer's theorem, a kernel K is positive semi-denite
if and only if there exists a mapping such thatK (xi
, xj
) = (xi
), (xj
) , xi
, xj
Xwhere , denotes a scalar dot productIs an inner product in a suitable feature space
V (H) =
H
(1)m H(1) H(r) m H(r) 1, .., 1,
0, ...0 , ..., , 1, ..., 1, 0, ...0 rst bin last bin
p-dimensional binary vector, p = m rm: total number of points in the histogram
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Feature Extraction
1
Weak features
Oriented to edge points
Points whose gradient magnitude in given direction exceeds a
minimum threshold
Extract edge points at two scales and eight orientations
M = 16 channels
2
Strong features
SIFT descriptor
Dense regular grid
16 16 pixel patches
Vocabulary sizes: M = 200, M = 400 (k-means )
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Experiments
Three diverse datasets:
fteen scene categories
Caltech-101 [3]
Graz
Perform all processing in grayscale
All experiments are repeated ten times with dierent randomly
selected training and test images
The nal result is reported as the mean and standard deviation
of the results from the individual runs
Multi-class classication: Support vector machine
(SVM),trained using the one-versus-all rule
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Scene Category Recognition
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Scene Category Recognition
Spatial pyramid kernel and strong features with M = 200
Latent semantic analysis (pLSA): Dimensionality reduction of
the feature space from 200 to 60
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Confusion table
Confusion occurs between the indoor classes (kitchen, bedroom, living room)
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Retrieval from the scene category database.
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Caltech-101
This database contains from 31 to 800 images per category
The most diverse object database
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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The Graz database
High intra-class variation
Two object classes, bikes (373 images) and persons (460
images)
Train detectors for persons and bikes on 100 positive and 100
negative images
Results for strong features:
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Conclusions
This paper presents a method for recognizing scene categories
based on approximate global geometric correspondence
Ecient algorithm, the computational complexity of the kernel
is linear in the number of features
Does very well on global scene classication tasks
When a class is characterized by high geometric variability, it is
dicult to nd useful global features
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories