1 Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval Keita Fukuda, Tetsuya...
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Transcript of 1 Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval Keita Fukuda, Tetsuya...
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Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval
Keita Fukuda, Tetsuya Takiguchi, Yasuo ArikiGraduate School of Engineering, Kobe University, Japan
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• Introduction– Purpose of Multiple Classifier based on Fuzzy C-means
– Overview of our flower recognition system
• Proposed Method– Each Classifier
– Fuzzy C-means
• Experiments
• Summary and Future Work
Table of contentsTable of contents
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IntroductionIntroduction
What is this flower ?Retrieval system requires “keywords.” But it is difficult to get “keywords” from “images.”
We take a flower picture and send it to a system. We receive flower image information there and then immediately.
We are focusing on flower image retrieval system
In our proposed method
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Conventional techniquesConventional techniques
Conventional method• Using the same features for classification.⇒But flowers have various shape.
• We propose multiple classifier which selects important features for each flower type and weights the importance on each classifier using Fuzzy c-means.
It is required to select important features according to flower type.
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Overview of our systemOverview of our system
Send image
Receive information
Database
Flower region extraction
Color and shape features extraction
Similarity by multiple classifier
contents based flower image retrieval
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Flower region extractionFlower region extraction
A large color regions locating at near center are extracted as flower region
Color and Shape features are computed on them.
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Feature extractionFeature extraction
Color feature
0.42
0.03
0.00
10
10
Distribution histogram
Shape feature
d
ld
Power
Freq
Power spectrum
Fourier transform
l: contour pixel
d: distance from G to contour
Gravity to contour
100 segments
G
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Similarity for each classifier is calculated
Recognition with multiple classifier (1)Recognition with multiple classifier (1)
Query image
Image
Multiple classifier
BF
CF
AF 0.03
0.93
0.04
iAV
iBV
iCV
Similarity for classifier
Membership of query image
(Weight)
)(iM
i
jF
Similarity
Features, Information, Similarity
…Database
We define 3 classifiers for 3 flower types
Membership of a query image in each type is obtained as weight for each similarity
Linearly coupled similarity matching of 3 classifiers
Which types is a query image
associated with ?
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Each flower typeEach flower type
Type Classifier For Similarity
A FA “Near circle” flowers ViA
B FB “Clear one petal” flowers ViB
C FC “many petals” flowers ViC
The similarity in each classifier is computed using Weighted Histogram Intersection. The value of weight represents the
difference of each classifier
We define 3 classifiers for 3 flower types:
A: “Near circle” B: “Clear one petal” C: “Many petals”
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Each ClassifierEach Classifier
Gaussian Weight
Histogram Intersection
Query image
image i
In type AIn type BCharacteristics
Peak (5) = the number of petal
Weight of low frequency rangeWeight of band frequency range based on peak
Important similarity
In type C
Weight of high frequency range
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Query image
Image
Multiple classifier
BF
CF
AF 0.03
0.93
0.04
iAV
iBV
iCV
Similarity for classifier
)(iM
i
jF
Similarity
Features, Information, Similarity
…Database
Membership of query image
(Weight)
Weight for each similarity is membership of a query image in type A, B and C
⇒ It is difficult that all flowers are classified into one of 3 types clearly.
Recognition with multiple classifier (2)Recognition with multiple classifier (2)
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Fuzzy C-meansFuzzy C-means
N
i
C
jji
mijm cxuJ
1 1
2
It is based on minimization of the following objective function:
Fuzzy partitioning is carried out through an iterative optimization of the objective function, with the update of membership uij and the cluster centers cj
N
iijij uiallforu
1
1,0,1
Membership property is
Data elements can belong to more than one cluster. associated with each element is a set of membership.
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Fuzzy C-means For flower retrieval systemFuzzy C-means For flower retrieval system
1. Database images are clustered using fuzzy c-means.
2. Membership of a query image is computed.
membership
{0.88, 0.05, 0.08}
Data elements
{0.43, 0.41, 0.12}
{0.08, 0.12, 0.80}
C: ”Many petals”
B: ”Clear one petal”
A: ”Near Circle”
Membership of a query image is obtained as weight for each similarity {0.03, 0.93, 0.04}
Input data: shape features {compactness, entropy, average} Output data: membership of the image in each type.
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Query image
Image
Multiple classifier
BF
CF
AF 0.03
0.93
0.04
iAV
iBV
iCV
Similarity for classifier Membership
(Weight)
)(iM
i
jF
Similarity
Features, Information, Similarity
…Database
iCiBiA VVViM 04.093.003.0)(
Linearly coupled similarity matching of 3 classifiers is calculated. This example, the similarity between image “i” and a query image:
Recognition with multiple classifier (3)Recognition with multiple classifier (3)
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Result informationResult information
Result information are shown to users up to fifth rank based on the similarity M(i)
Input image Result information
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Experimental conditionExperimental condition
• Flower images of 120 species with each 4 samples.
(i.e. 480 images in total).
Four Cross validation (evaluate : cumulative recognition)
• One sample is used as a query image (120).
• The others are used as the database images (120×3).
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Conventional methodConventional method
• Compactness
• The number of petal (peak)
• Moment
• The ratio of the shortest width over the longest
Largest segment
• X coordinate
• Y coordinate
• Its distributed value
2nd Largest segment
• X coordinate
• Y coordinate
• Its distributed value
Shape features Color featuresy
x
peak
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Experimental resultExperimental result
1st 3rd 5th 10thConventional method 33.8 59.6 69.4 80.8
Multiple classifier
No fuzzy 39.8 67.1 78.1 89.4fuzzy 42.7 69.6 81.3 92.5
Proposed method Conventional methodquery
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New concept:multiple classifier which select important features for each flower type
SummarySummary
Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval
In future work: research for more than three classifiers