Similarity based leaf image retrieval using multiscale R-angle description

Cao, Jie; Wang, Bin; Brown, Douglas

doi:10.1016/j.ins.2016.09.023

Cited by 37 publications

(29 citation statements)

References 30 publications

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“…However, it is a worthy topic to determine the value of critical parameter K reasonably. In reference [19], an optimised multi‐scale generation method is used to locate the intersections of the shape with a circle of radius R centred at points sampled around the contour. The multi‐scale R ‐angle is produced using the intersections and sampled points, and can describe contour curvature by measuring the sine of the angle between the intersections.…”

Section: Related Workmentioning

confidence: 99%

Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Wang

2019

IET image process

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Leaf image identification is a significant and challenging research work. Here, a unified multi‐scale method is proposed to capture leaf geometric information for plant leaf classification and image retrieval. For each point on the leaf contour, the unified multi‐scale method utilises a simple yet effective three‐step strategy to locate corresponding neighbour points. The descriptor extracted using these neighbour points can provide a coarse‐to‐fine description of leaf contours and is of multi‐scale characteristic intrinsically. More importantly, there is no scale parameter to be adjusted in the method, and hence no optimisation procedure is required. The proposed method is applied to three well‐known contour features to capture geometric information of leaves, including angle, arch‐height, and triangle‐area representation. FFT is applied on the features in unified multi‐scale method for convenient and fast leaf matching. Leaf classification and image retrieval experiments are conducted on four challenging leaf datasets to test the proposed method and evaluated using three standard performance evaluation metrics. The experimental results and comparisons with the state‐of‐art methods indicate that the unified multi‐scale method has remarkable performance.

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Section: Related Workmentioning

confidence: 99%

Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Wang

2019

IET image process

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“…Caoa et al, 18 for the purpose of leaf shape classification, used the R‐angle. The curvature of the contour can be elucidated by the estimation of the angle between the intersections of the shape contour having a circle with radius R, which is centered at points sampled in the region of the contour that represents the R‐angle.…”

Section: Literature Reviewmentioning

confidence: 99%

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

Kumar¹,

Sumithra

Saranya³

2019

Concurrency and Computation

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Summary This paper proposes an automatic classification technique that uses leaf images some medicinal plants. It is primarily the core reason that drives the research presented here, including the introduction of new innovative segmentation and classification techniques that are deployed to facilitate automatic detection. The major aim of the work is to introduce a new leaf disease prediction technique. The study conducted here a unique but effective image segmentation, feature extraction, as well as plant leaf disease classification. The proposed approach initially preprocesses leaf images of plants thereafter which the diseased sections of the plant are segmented by deploying Particle Swarm Optimization (PSO)–based fuzzy c means segmentation (PSO‐FCM), Gaussian Mixture Model (GMM)–based background subtraction. Vein and shape features, edge‐based feature extraction, and texture characteristics or texture features (TF) are computed. This methodology classifies the leaves of medicinal plants by deploying the Multiple Kernel Parallel Support Vector Machine (MK‐PSVM) classifier. The classifier is implemented via the use of MATLAB classifier. The results are measured using the accuracy, sensitivity, specificity, precision, and F‐measure metrics. Experimental results depict that the classifiers that have been proposed here achieve a higher classification accuracy enabling leaf detection.

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“…This property is not suited to natural images of leaves and flowers, and is only applicable to text and cartoons. Multi-scale R-angle [27] descriptor, compared to all the other descriptors, is intrinsic to shape contours under translation, rotation and scaling, because the other methods need normalization for scaling.…”

Section: Multi-scale Descriptorsmentioning

confidence: 99%

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Thyagharajan¹,

Raji²

2018

Arch Computat Methods Eng

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Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves. Table 2 Inferences of data sources Visual descriptors of leaf and classification No. of papers selected Geometrical descriptors 30 Leaf shape/tip/base/venation 40 Texture/texton 25

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Similarity based leaf image retrieval using multiscale R-angle description

Cited by 37 publications

References 30 publications

Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Unified multi‐scale method for fast leaf classification and retrieval using geometric information

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

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