Classification of leaf images | Semantic Scholar (2024)

This work investigates shape analysis methods for retrieving images and develops an approach for doing so that first finds out the corners of the leaf by "Harris corner detection" and determines the convex hull by joining these "corner points".

The development of an Android application that gives users the ability to identify plant species based on photographs of the plant’s leaves taken with a mobile phone is described.

This paper proposes an efficient computer-aided Plant Image Retrieval method based on plant leaf images using Shape, Color and Texture features intended mainly for medical industry, botanical gardening and cosmetic industry, which outperforms the recently developed methods.

The ideal case approach in plant classification and recognition that is not only applicable in the real world, but also acceptable in the lab is delivered.

A new leaf image based arable and tree crops identification process based on CART and Radial basis function for classification accuracy is suggested, which gives average accuracy of 88.21% when tested with 195 instances of 6 species of arable or tree crops leaf images.

An Android application that gives users the ability to identify plant species based on photographs of the plant’s leaves taken with a mobile phone using an algorithm that acquires morphological features of the leaves, computes well documented metrics such as the angle code histogram (ACH), then classifies the species based on a novel combination of the computed metrics.

This work proposes a leaf recognition method which uses a new features set that incorporates shape, color and texture features, and has been tested on Flavia dataset, showing excellent performance both in terms of accuracy that often reaches 100%, and in Terms of speed, less than a second to process and extract features from an image.

A semi-SLLE is proposed and is applied to plant classification based on leaf images and shows that the proposed algorithm performs very well on leaf image data which exhibits a manifold structure.

A feed forward neural network is used to automate the leaf recognition for plant classification and the classification accuracy of the proposed Normalized Cubic Spline Feed Forward Neural Network (NCS - FFNN) is compared with RBF, CART and MLP.

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Experimental results on 1400 leaf images from 140 plants show that the proposed approach can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method.

A new approach to content-based retrieval of medical images from a database is described, in which similarity is learned from training examples provided by human observers, and the use of neural networks and support vector machines to predict the user's notion of similarity is explored.

This paper proposes an approach with the capability of matching partial word images to address two issues in document image retrieval: word spotting and similarity measurement between documents.

A set of novel features and similarity measures allowing query by image content, together with the QBIC system, and a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance.

Experiments show image retrieval mechanisms based on a combination of texture and color features to be as effective as other methods while computationally more tractable.

This work applies a radial-basis function (RBF) network for implementing an adaptive metric which progressively models the notion of image similarity through continual relevance feedback from users, and shows that the proposed methods not only outperform conventional CBIR systems in terms of both accuracy and robustness, but also previously proposed interactive systems.

The original contributions of this paper is the generalization of the curvature scale space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves.

A transformation invariant metric recently proposed in the machine learning literature to measure the distance between image manifolds - the tangent distance (TD) - is analyzed and shows that it is closely related to alignment techniques from the motion analysis literature.

Experimental results indicate that structures of leaves and shapes of their components can be considered as landmarks to recognize the species of plants.

A simple yet novel matching algorithm based on the multiresolution histogram that uses the differences between histograms of consecutive image resolutions to achieve or exceed the performance obtained with more complicated features.

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