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• W4323967460 abstract "The automatic classification of plant species using leaf’s images is a rather complex task because of the high variability of species and the high inter-species similarity of the descriptors to be extracted. In order to extract robust descriptors, convolutional neural networks combine several architectures adapted to the extraction of shape and texture descriptors. These increasingly complex architectures are generally transpositions to the problems of the classification of plant species of models previously trained on image bases. As a result, the performance of these models is linked to the relevance of the database used. In this paper, we propose a new simple architecture capable of simultaneously extracting shape and texture descriptors in a single model. This model is based on the use of several different resolution levels of the energy layer initially introduced by Andrearczyk et al. in the analysis of dynamic textures. This makes it possible to efficiently extract the shape and texture descriptors in a single architecture of convolutional neural networks, less complex in terms of the number of parameters to be determined. The tests carried out on Flavia give us good classification rates of 99%. They are 97.10% on MalayaKew D1, 99.68% on MalayaKew D2, and 97.00% on LeafSnap. These results show the excellent capabilities of our model on some of the most commonly used image databases." @default.
• W4323967460 created "2023-03-13" @default.
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• W4323967460 date "2023-01-01" @default.
• W4323967460 modified "2023-10-18" @default.
• W4323967460 title "Global Approach of Shape and Texture Features Fusion in Convolutional Neural Network for Automatic Classification of Plant Species Based on Leaves Images" @default.
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• W4323967460 doi "https://doi.org/10.1007/978-981-19-9225-4_48" @default.
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