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

Autor(es)

Florindo, Joao ^[1] ; Metze, Konradin ^[2]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Entropy; v. 23, n. 10 OCT 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 20/01984-8 - Introduzindo elementos de geometria fractal em redes convolucionais profundas: uma aplicação ao reconhecimento e categorização do Câncer de Pulmão
• Beneficiário: Joao Batista Florindo
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Beneficiário: João Marcos Travassos Romano
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia