GTCLC: leaf classification method using multiple descriptors

Kalyoncu, Cem; Toygar, Önsen

doi:10.1049/iet-cvi.2015.0414

Cited by 24 publications

(18 citation statements)

References 32 publications

(75 reference statements)

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“…The resultsshow that the proposed method, the GIST features with PCA = 40% and the Cosine KNNclassifier has accurate results. Of course, the accuracy of GTCLC method [21] is very close to the proposed method, butthe problem with this method is its low computational speed, because this method isa combination of many features and related pre‐processes that makes the featureextraction time of each image is about 0.94 s, which is nearly four times lower thanthe proposed method.…”

Section: Resultsmentioning

confidence: 98%

Plant leaf classification using GIST texture features

Kheirkhah

Asghari

2019

IET Computer Vision

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The leaves of plants have rich information in recognition of plants. In general, agriculture experts accomplish information extraction from the leaves. Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features. Then, the principal and suitable features are selected by principal component analysis (PCA) algorithm. In the classification step, three different approaches such as Patternnet neural network, support vector machine, and K-nearest neighbour (KNN) algorithms were applied to the extracted features. For evaluation of the authors' approach, they applied their proposed algorithm on three famous datasets. In comparison to some widely used features, the results show that their approach outperforms the other methods in the case of the time and the accuracy. The best results were achieved by applying PCA algorithm to GIST feature vector and using the Cosine KNN classifier.

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Section: Resultsmentioning

confidence: 98%

Plant leaf classification using GIST texture features

Kheirkhah

Asghari

2019

IET Computer Vision

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“…To evaluate the impact of ROI extraction process on hand vein recognition performance, characterisation must be carried on. In this context, we use as illustration a vein recognition system with local binary pattern (LBP) descriptor as feature extractor, because it is among the most reliable methods for textural image recognition systems [24–27]. The mathematical LBP code of each pixel is defined as follows:

LB P_{P, R} = \sum_{k = 0}^{P - 1} 2^{k}, s false(g_{k} - g_{c} false), s false(x false) = ((, \begin{matrix} 1em4pt \\ 1, & if x \geq 0 . \\ 0, & else . \end{matrix})

where R is the radius of the neighbourhood, P represents the number of neighbours, s is defined by the signs of the differences between neighbourhood pixels and the centre pixel,

g_{k}

is grey level value of its neighbours,

g_{c}

represents the grey value of the central pixel.…”

Section: Experimental Validation and Discussionmentioning

confidence: 99%

Dynamic ROI extraction method for hand vein images

Damak

Trabelsi

Damak

et al. 2018

IET Computer Vision

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The region of interest (ROI) extraction is important in hand vein recognition system. The main challenges for accurate extraction of the vein region are to overcome variability in hand size, lighting conditions, orientation, appearance, noisy background, and non-uniform grey levels in foreground region. Here, we propose a new dynamic hand vein ROI extraction, preserving the whole vein area. A hand segmentation process robust to the mentioned challenges, contributing to an accurate definition of hand edge delimitations is proposed. Our approach is validated on both dorsal vein Bosphorus database and palm vein Vera database. Our proposed method accuracy is ∼98% for Bosphorus database and 90% for Vera database. To illustrate the efficiency of the proposed ROI extraction, we insert it as a first block in a hand vein recognition system. Then, a comparison study at system level with recent approaches is carried on, showing an improvement of the whole system area under the curve by a rate of 12% and 2% for Bosphorus and Vera databases, respectively. The speed performances demonstrate a mean run time of 0.73 s for Bosphorus database and 1.2 s for Vera database, proving that the proposed method can be conveniently used on a real-time application.

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“…Hence, the detailed comparisons are listed in Table 5. GTCLC [55] is a leaf classification method using multiple descriptors. Cem Kalyoncu et al proposed a new local binary pattern (LBP) descriptor, and they combined it with geometric, shape, texture, and color features for leaf recognition.…”

Section: Comparison Of Different Methodsmentioning

confidence: 99%

Leaf Image Recognition Based on Bag of Features

et al. 2020

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Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

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GTCLC: leaf classification method using multiple descriptors

Cited by 24 publications

References 32 publications

Plant leaf classification using GIST texture features

Plant leaf classification using GIST texture features

Dynamic ROI extraction method for hand vein images

Leaf Image Recognition Based on Bag of Features

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