Scholarlink

<p>Islamic geometric patterns (IGPs) are one of the most elegant forms of art that reflect the Islamic cultural and religious heritage. These patterns are characterized by their complexity and precision. Which makes them a source of inspiration for the design of decorations in light of technological progress. Geometric patterns have never been distinguished before using deep Learning. This data is the first to be used and classified. The research includes classifying IGP into 8 classes Arabesque, Tessellation, Euclidean tiling by convex regular polygons, Koch snowflake, logarithmic spiral, Mandelbrot set, Pythagorean Fractal Tree, Sierpinski Triangles using Transfer Learning. Six deep Learning modes are used VGG19, Mobilenetv2, inception_resnet_v2, xception, NASNetLarge, and Rasnet v2. Adjusting the initialized performance parameters for all model to be able to compare between them. The evaluated performance parameters are training accuracy and loss, evaluation accuracy and loss, precision, recall, confusion matrix, and F1 score. Rasnetv2 gives the higher accuracy, F1 score at small size data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using transfer learning to determine the type of mathematical fractals image of Islamic geometric patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月24日

查看全文
  • Seham Ebrahim

摘要

Islamic geometric patterns (IGPs) are one of the most elegant forms of art that reflect the Islamic cultural and religious heritage. These patterns are characterized by their complexity and precision. Which makes them a source of inspiration for the design of decorations in light of technological progress. Geometric patterns have never been distinguished before using deep Learning. This data is the first to be used and classified. The research includes classifying IGP into 8 classes Arabesque, Tessellation, Euclidean tiling by convex regular polygons, Koch snowflake, logarithmic spiral, Mandelbrot set, Pythagorean Fractal Tree, Sierpinski Triangles using Transfer Learning. Six deep Learning modes are used VGG19, Mobilenetv2, inception_resnet_v2, xception, NASNetLarge, and Rasnet v2. Adjusting the initialized performance parameters for all model to be able to compare between them. The evaluated performance parameters are training accuracy and loss, evaluation accuracy and loss, precision, recall, confusion matrix, and F1 score. Rasnetv2 gives the higher accuracy, F1 score at small size data.