Download MiningPetroglyphs_KDD`09 - University of California, Riverside

Augmenting the
Generalized Hough Transform
to Enable the Mining of Petroglyphs
Qiang Zhu, Xiaoyue Wang, Eamonn Keogh, 1Sang-Hee Lee
Dept. Of Computer Science & Eng., 1Dept. of Anthropology
University of California, Riverside
Outline
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Motivation
Approach
Evaluation
Conclusion
Motivation(1)
-applications
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Petroglyphs are one of the earliest
expressions of abstract thinking.
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Providing a rich source of information:
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climate change
existence of a certain species
patterns of human’s migrations and interactions
Motivation(2)
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-difficulties
Progress in petroglyph research has been
frustratingly slow.
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due to their extraordinarily diverse and complex
structure
most matching algorithms can not capture the
similarity of petroglyphs
for those that can, even in limited cases, do not
scale to large collections
Approach
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How to preprocess the raw data?
How to define the distance measure?
How to speed up?
Preprocessing(1)
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With rare exceptions, petroglyphs do not lend
themselves to automatic extraction with
segmentation algorithms.
The border of this rock may
be recognized as the edge
of this petroglyph
PetroAnnotator
Load the raw image into our human computation
tool
PetroAnnotator (cont.)
Draw an approximate boundary around
object, and then trace the shape
Preprocessing(2) -downsampling
A
B
(A) Two overlaid skeleton traces (340 by 250) of the same image of a
Bighorn sheep. Less than 3.5% of the pixels from each image
overlap.
(B) The same two images after downsampling (30 by 23).
75.6% of the pixels (denoted by black) are common to both.
Distance Measure
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essentially makes no assumption about the data
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open/closed boundaries
connected/disconnected shapes
correctly captures the similarity
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-why GHT?
subjective/objective similarity on unlabeled/labeled
datasets
tightly lower bound the distance
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allowing for very efficient searches in large datasets
Classic GHT
GHT is a useful method for two dimensional
arbitrary shape detection.
Q
C
(1) Find the “star-pattern”
R
R
(2) Superimpose & Accumulate
C
A
0
1
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
2
2
1
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
1
2
3
2
1
0
0
0
0
0
0
1
1
1
0
0
0
1
0
0
1
2
3
2
1
0
1
1
1
0
(3) Find the “peak”
Q
C
R’
R
A
0
1
1
1
0
0
0
1
0
0
1
2
3
2
1
0
1
1
1
0
A Basic Distance Measure
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Classic GHT doesn’t explicitly encode a
similarity measure
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We can simply define a GHT-based distance:
minimal unmatched edge points (MUE) =
number of edge points in Q – maximal matched edge points
= 4 – 3 = 1 (for our toy example)
A New Cell Incrementation Strategy
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When can we obtain the value of a particular cell
in the accumulator?
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In the classic GHT, until the end of all incrementation
Is it possible to obtain the value one by one?
Need to check all positions that are possible to increase the
cell value
Q
C
?
Lower Bound
Q
C
SigQx = 2 2 4 2 2
?
?
?
?
?
?
?
?
?
SigCx = 0 0 0 3 2 2 2 3 0 0 0
In this column Q needs 2 pixels in C, and has 3
In this column Q needs 2 pixels in C, and has 2
In this column Q needs 4 pixels in C, and has only 2
In this column Q needs 2 pixels in C, and has 2
In this column Q needs 2 pixels in C, and has 3
2 2 4 2 2
Minimal missed points: 0 + 0+ 2+ 0+ 0 = 2
Time Complexity
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Classic GHT
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O(NQ×NC+S2)
superimpose all query vectors to all edge points in the
candidate image
Lower bound GHT
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O(S2)
compare one-dimensional signatures
further reduced by early abandon and shifting order
one to two orders of magnitude speed-up
Variants on the Basic Distance Measure
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Query-by-Content:
1
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 N  MUE(Q, C ) N C / N Q
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Dnn (Q, C )   Q
1
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 N Q  MUE(Q, C )
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if N C  N Q
otherwise
Clustering:
Dclustering (Q, C )  NQ  NC  [ Dnn (Q, C )  Dnn (C , Q)]
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Finding Motifs:
Dmotifs (Q, C )  ( NQ  NC ) / 2  ( NQ  MUE(Q, C ))
Evaluation
We performed three sets of experiments：
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Evaluation of Utility
-on unlabeled data
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Evaluation of Accuracy
-on labeled data
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Evaluation of Scalability
-on synthetic data
Evaluation of Utility (1)
Atlatls
Anthropomorphs
Bighorn Sheep
A clustering of typical Southwestern USA petroglyphs
(1) Our GHT-based
distance measure
correctly groups all
seven pairs
(2) The higher level
structure of the
dendrogram also
correctly groups similar
petroglyphs
Evaluation of Utility (2)
a
SC
WY
b
c
d
e
f
g
h
Evaluation of Utility (3)
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Whether our distance measure can find meaningful
motifs?
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2,852 real petroglyphs
4,065,526 possible pairs
52 top motifs (0.00128%) by motif cutoff
0
50
Motif Cutoff
100
150
200
Evaluation of Accuracy
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NicIcon dataset
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24,441 images
14 categories
33 volunteers
234×234 pixels
WD/WI tests
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-datasets
Farsi digits dataset
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0
From 11,942 registration
forms
60,000 digits for training
20,000 digits for testing
54×64 pixels (largest MBR)
1
2
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4
5
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8
9
(1) Test the Downsampling Size
Error Rate (%)
30
20
WD
10
WI
0
5
10
20
30
40
50
60
70
Resolution (R×R) of Downsampled Images (NicIcon)
80
Error Rate (%)
16
12
8
4
2
5
10
20
Resolution (R×R) of Downsampled Images (Farsi)
30
In both datasets,
the error rate of
one-nearestneighbor test
varies little once
the resolution is
greater than
10×10
(2) Competitive accuracy
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NicIcon dataset
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Error rate for WD: 4.78%
8.46% for WI
The dataset creators tested
on the online data using
three classifiers.
Only one of them (DTWB) is
better, however, slower
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Farsi digits dataset
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Error rate: 4.54%
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Borji et al. performed
extensive empirical tests on
this dataset
Of the twenty reported error
rates, the mean was 8.69%
Only four beat our approach,
but need to set at least six
parameters
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Evaluation of Scalability -datasets
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We made 8 synthetic petroglyph datasets
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Based on 22 classic petroglyphs
Duplicated by 10 volunteers on a tablet
Applied a Random Polynomial Transformation
Containing up to 1,280,000 objects
(1) Querying by Content
100
Prune Rate (%)
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Leave-one-out one-nearest-neighbor test.
Repeated the test for 10 times on each dataset.
80
Max Prune Rate
60
Avg Prune Rate
Min Prune Rate
40
10K
20K
40K
80K
160K
320K
640K
1280K
Size of Synthetic Petroglyphs Datasets
% to Brute Force Time
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18
14
10
6
2
10K
20K
40K
80K
160K
320K
Size of Synthetic Petroglyphs Datasets
640K
1280K
(2) Finding Motifs
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A brute force algorithm requires time quadratic in the size of
dataset.
By using the triangular inequality of our distance measure, we
only need to calculate a tiny fraction of the exact distance.
Speed Up (times)
120000
80000
40000
0
10K
20K
40K
80K
160K
320K
640K
Size of Synthetic Petroglyphs Datasets
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Even for the smallest dataset:
-our algorithm is 712 times faster
-we can prune 99.84% of the calculations
1280K
Conclusion
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In this work we considered, for the first time,
the problem of mining large collections of
rock art.
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Introduced a novel distance measure
Found an efficiently computable tight lower bound
to this measure
Enabled mining large data archives effectively
All datasets and the code can be downloaded from:
http://www.cs.ucr.edu/~qzhu/petro.html
Thanks for your listening !
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