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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Using Non-Additive Entropy to Enhance Convolutional Neural Features for Texture Recognition

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Florindo, Joao [1] ; Metze, Konradin [2]
Total Authors: 2
[1] Univ Estadual Campinas, Inst Math Stat & Sci Comp, BR-13083859 Campinas - Brazil
[2] State Univ Campinas UNICAMP, Fac Med Sci, BR-13083894 Campinas - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Entropy; v. 23, n. 10 OCT 2021.
Web of Science Citations: 0

Here we present a study on the use of non-additive entropy to improve the performance of convolutional neural networks for texture description. More precisely, we introduce the use of a local transform that associates each pixel with a measure of local entropy and use such alternative representation as the input to a pretrained convolutional network that performs feature extraction. We compare the performance of our approach in texture recognition over well-established benchmark databases and on a practical task of identifying Brazilian plant species based on the scanned image of the leaf surface. In both cases, our method achieved interesting performance, outperforming several methods from the state-of-the-art in texture analysis. Among the interesting results we have an accuracy of 84.4% in the classification of KTH-TIPS-2b database and 77.7% in FMD. In the identification of plant species we also achieve a promising accuracy of 88.5%. Considering the challenges posed by these tasks and results of other approaches in the literature, our method managed to demonstrate the potential of computing deep learning features over an entropy representation.</p> (AU)

FAPESP's process: 20/01984-8 - Introducing elements of fractal geometry into deep convolutional neural networks: an application to the recognition and categorization of Lung Cancer
Grantee:Joao Batista Florindo
Support Opportunities: Regular Research Grants
FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
Grantee:João Marcos Travassos Romano
Support Opportunities: Research Grants - Research Centers in Engineering Program