Leaf Species Classification Based on a Botanical Shape Sub-classifier Strategy

Liu, H.; Coquin, Didier; Valet, Lionel; Cerutti, Guillaume

doi:10.1109/icpr.2014.266

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…The accuracy of the system without the fusion features was found to be 59.6% for fuzzy local binary pattern and 50.8% for fuzzy colour histogram, respectively. In another study, leaf species classification was conducted using a botanical shape sub-classifier strategy [43]. The implementation of fusion strategy and its corresponding random-forest-based sub-classifiers as part of a leaf recognition system.…”

Section: Probabilistic Neural Network (Pnn)mentioning

confidence: 99%

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Review on Techniques for Plant Leaf Classification and Recognition

et al. 2019

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

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Section: Probabilistic Neural Network (Pnn)mentioning

confidence: 99%

“…The fusion technique provides a significant accuracy enhancement compared to other proposed methods, while providing necessary information for educational purposes. [43]…”

mentioning

confidence: 99%

Review on Techniques for Plant Leaf Classification and Recognition

et al. 2019

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“…This approach, as mentioned earlier, relies on the user to select the base point and other reference points for segmenting and aligning the leaf. Another partial shape approach was proposed in [19], using a sub-classifier strategy based on the local and the global shape criteria in the flora books. Four sets of attributes have been considered to extract botanical labels input into a Random Forests classifier (RF) to identify the species of a plant.…”

Section: Related Workmentioning

confidence: 99%

Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

Hamid

Al-Haddad

2019

ELCVIA

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The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. Several approaches have been proposed, and the majority focused on global shape features. However, one challenge that faces this task is the high interclass similarity amongst the leaves of different species in terms of the global shape. Furthermore, there always has been an obstacle against full automation as several approaches require user intervention to align the leaf. Therefore, a new set of Quartile Features (QF) is proposed in this paper to describe the partial shape of the leaf, in addition to an automated alignment approach to automate the system. The QF are extracted from the horizontal and vertical leaf quartiles to describe the partial shape of the leaf and the relations among its parts. The well-known Flavia dataset has been selected for the evaluation of the proposed system. The experimental results indicate the ability of the proposed alignment algorithm to align leaves with different shapes and maintain a correct classification accuracy regardless of the orientation of the input leaf samples. Furthermore, the proposed QF indicated promising results by increasing the accuracy of the classification by a range of approximately 26% to 30% when combined with Hu's Moment Invariants, using k-fold cross-validation technique.

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“…Although there has been some work on flower classification [45] and large scale plant identification from learned categories [17], classifying leaves in order to determine the species is the most common approach. There is a large body of literature ( [40], [44], [25], [30], [8], [53], [11], [10], [43], [9], [34], [55], [61], [54], [28], [63]) over the last two decades on leaf recognition for plant species identification. Leaf classification was first reported by Mokhtarian et al [40].…”

Section: Related Workmentioning

confidence: 99%

“…The method achieves good performance on a public database of Swedish leaves, including leaves that are collected from 100 tree species. Some recent work ( [34], [54]) developed leaf recognition systems for mobile applications. Recently Zhao et al [61] demonstrated a computationally fast technique for leaf recognition.…”

Section: Related Workmentioning

confidence: 99%

Partially Occluded Leaf Recognition via Subgraph Matching and Energy Optimization

Chaudhury,

Barron

2017

Preprint

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We present an approach to match partially occluded plant leaves with databases of full plant leaves. Although contour based 2D shape matching has been studied extensively in the last couple of decades, matching occluded leaves with full leaf databases is an open and little worked on problem. Classifying occluded plant leaves is even more challenging than full leaf matching because of large variations and complexity of leaf structures. Matching an occluded contour with all the full contours in a database is an NP-hard problem [Su et al. ICCV2015], so our algorithm is necessarily suboptimal.First, we represent the 2D contour points as a β-Spline curve. We extract interest points on these curves via the Discrete Contour Evolution (DCE) algorithm. To find the best match of an occluded curve with segments of the full leaf curves in the database, we formulate our solution as a subgraph matching algorithm, using the feature points as graph nodes. This algorithm produces one or more open curves for each closed leaf contour considered. These open curves are matchable, to some degree, with the occluded curve. We then compute the affine parameters for each open curve and the occluded curve. After performing the inverse affine transform on the occluded curve, which allows the occluded curve and any subgraph curve to be "overlaid", we then compare the shapes using the Fréchet distance metric. We keep the best η matched curves. Since the Fréchet distance metric is cheap to compute but not perfectly correlated with the quality of the match, we formulate an energy functional that is well correlated with the quality of the match, but is considerably more expensive to compute. The functional uses local and global curvature, angular information and local geometric features. We minimize this energy functional using the well known convexconcave relaxation technique. The curve among the best η curves retained. that has the minimum energy, is considered to be the best overall match with the occluded leaf. Experiments on three publicly available leaf image database shows that our method is both effective and efficient, outperforming other current stateof-the-art methods. Occlusion is measured as a percentage of the overall contour (and not leaf area) that is missing. We show our algorithm can, for leaves that are up to 50% occluded, still identify the best full leaf match from the databases.

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Leaf Species Classification Based on a Botanical Shape Sub-classifier Strategy

Cited by 16 publications

References 9 publications

Review on Techniques for Plant Leaf Classification and Recognition

Review on Techniques for Plant Leaf Classification and Recognition

Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification

Partially Occluded Leaf Recognition via Subgraph Matching and Energy Optimization

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