4. Introduction
Geometric features are successfully used in leaf classification in the literature Low
dimensionality is the major advantage of geometric features. However, geometric
features can only describe coarse shape of the leaf such as its similarity to a circle.Using
moment invariants and contour-based shape descriptors adds more details to leaf
descriptor. However, they cannot distinguish between leaf margin and noise.
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5. In this resarch…
three methods, namely support vector machines, penalized discriminant analysis and random
forests methods are analyzed. The results demonstrate that random forests method reaches up
to 90% accuracy.
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6. Past Method
Another plant identification system called Leafsnap is proposed in. This paper details a
complete system from acquisition to presenting the results. However, the speed of this system is
a limiting factor as it takes 5.4 s for a single leaf classification.
Another method that works on leaf textures.In this method, Gabor co-ccurrences are used as
features. For classification,this method uses KNN with Jeffery-divergence distance measure. The
reported results display 85% accuracy on a hand selected leaf texture database containing 32
classes. Performingthis algorithm on randomly selected sections of leaves roduces 80% accuracy.
A probabilistic neural network system that works on geometric features is proposed in, which
extracts 12 geometric features from segmented leaf image. The research is performed on 32
kinds of plants with an accuracy of 90%. This method requires user to enter start and end points
of the midrib. Therefore, this method cannot be used for automated classification tasks.
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7. In this paper
we propose a leaf classification system that uses geometric features, Multi-scale Distance
Matrix (MDM) and moment invariants.Moment invariants and MDM cannot distinguish between
leaf margin and noise. In order to solve this issue, we propose five additional features that
describe leaf margins. We have also employed Linear Discriminant Classifier (LDC) for the reason
that it can work with different classes having different importance factor for features.
Compared to the state-of-the-art shape based leaf identification methods, our proposed
method has better performance in terms of accuracy. Additionally, the proposed method
employs LDC which has a lower computational complexity compared to many other classifiers.
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8. 2.Proposed method
Our proposed method consists of 3 steps
first step preprocessing step to prepare images using
Segmentation
Nois reduction
contour extraction
Corner detection
The second step
extracts features from the binary images
last step
performs actual classification
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9. 2.1. Preprocessing
Segmentation
Data set
Flavia : employed a simple adaptive threshold segmentation over blue channel (1907scanned images of 32
different plants)
(X,Y)∈ 𝐿𝑒𝑎𝑓 ⟺ blue Ix,y < 𝔼[ 𝑏𝑙𝑢𝑒 I]
Leafsnap : stalk removal (4375 samples containing 132 classes)
Noise removal
with algorithm
contour smoothing operator both to reduce noise and to detect smaller changes along the leaf blade
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13. 2.2.1.Moment invariants
Moment invariants define general shape characteristics of an image and are widely used as
shape features
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14. 2.2.2. Convexity
These features contain information about the overall leaf complexity
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15. 2.2.3. Perimeter ratio
Perimeter ratio feature is the ratio of the leaf perimeter to the leaf area
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18. 2.3. Classification
use LDC(Linear Discriminant Classifier) for leaf classification.
LDC is based on normal distribution and closely related to Quadratic Discriminant Classifier (QDC).
The main difference of LDC and QDC is that LDC assumes that the covariance matrices for all lasses
are the same. Although this assumption does not hold in real life scenarios
Common covariance can be calculated as follows:
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19. LDC training
the proposed method performs segmentation, noise removal, contour extraction and corner
detection. Using the data obtained from these steps, feature extraction methods are performed
to extract moment invariants, convexity, perimeter ratio, MDM, average margin distance and
margin statistics features as leaf descriptor. Using these leaf descriptors, an LDC is trained and
used for classification.
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21. 4. Conclusion
Compared to the other well-known classification systems, our proposed system has better
performance. Additionally, the proposed method has a comparable computational efficiency
with respect to the state-of-the-art systems.
it is also possible to incorporate texture related parameters to this system to distinguish leaves
that have identical shape but different texture.
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