geometric leaf classification

Geometric Leaf Classificationبرگ هندسی بندی طبقهاستاد: جناب آقای دکتر طالبی

رامین احمدی الری

20 November 2014

Computer Vision and Image Understanding 2014

Cem kalyoncu Osen Toygar

Estern mediterranean University Turkey

Ramin Ahmadi Lari GEOMETRIC LEAF CLASSIFICATION 1/20

IntroductionApplication

Identification of plant species

Identify species at risk


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.


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.


Past MethodAnother 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.


In this paperwe 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.


2.Proposed method Our proposed method consists of 3 stepsfirst step preprocessing step to prepare images using

SegmentationNois reductioncontour extractionCorner detection

The second stepextracts features from the binary images

last stepperforms actual classification


2.1. PreprocessingSegmentation

Data set Flavia : employed a simple adaptive threshold segmentation over blue channel (1907scanned images of 32

different plants)

(X,Y) blue Ix,y < I]

Leafsnap : stalk removal (4375 samples containing 132 classes)

Noise removal with algorithmcontour smoothing operator both to reduce noise and to detect smaller changes along the leaf blade


2.2. Feature extraction1) moment invariants2) Convexity3) perimeter ratio4) Multiscale distance matrix (MDM)5) average margin distance6) margin statistics


2.2.1.Moment invariants Moment invariants define general shape characteristics of an image and are widely used as shape features


2.2.2. Convexity These features contain information about the overall leaf complexity


2.2.3. Perimeter ratio Perimeter ratio feature is the ratio of the leaf perimeter to the leaf area


2.2.4. Multi-scale distance matrix


2.2. Margin statistics


2.3. Classificationuse 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:


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.


4. ConclusionCompared 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.


geometric leaf classification

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