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

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