Texture Classification Using Deep Convolutional Neural Networks
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 2 | Views: 28

Research Paper | Electronics & Communication Engineering | India | Volume 14 Issue 2, February 2025 | Popularity: 4.7 / 10


     

Texture Classification Using Deep Convolutional Neural Networks

Priyanka Kopanathi, Durga Ganga Rao Kola


Abstract: Texture classification is an essential task in computer vision and image processing, commonly applied in areas like image recognition and remote sensing. Traditional methods such as Local Binary Pattern (LBP) have limitations in capturing complex texture features. This paper proposes the use of deep convolutional neural networks (CNNs) for texture classification, which can extract complicate texture details and significantly improve classification accuracy. Experimental results on the KTH - TIPS database confirm the superiority of the proposed CNN - based method over conventional LBP technique.


Keywords: Texture Classification, Local Binary Pattern, Deep Learning, Confusion Matrix


Edition: Volume 14 Issue 2, February 2025


Pages: 169 - 175


DOI: https://www.doi.org/10.21275/SR25201114945



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Priyanka Kopanathi, Durga Ganga Rao Kola, "Texture Classification Using Deep Convolutional Neural Networks", International Journal of Science and Research (IJSR), Volume 14 Issue 2, February 2025, pp. 169-175, https://www.ijsr.net/getabstract.php?paperid=SR25201114945, DOI: https://www.doi.org/10.21275/SR25201114945

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