Open Access

ARTICLE

Big Texture Dataset Synthesized Based on Gradient and Convolution Kernels Using Pre-Trained Deep Neural Networks

Farhan A. Alenizi¹, Faten Khalid Karim^2,*, Alaa R. Al-Shamasneh³, Mohammad Hossein Shakoor⁴

1 Electrical Engineering Department, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Rafha Street, Riyadh, 11586, Saudi Arabia
4 Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran

* Corresponding Author: Faten Khalid Karim. Email:

Computer Modeling in Engineering & Sciences 2025, 144(2), 1793-1829. https://doi.org/10.32604/cmes.2025.066023

Received 27 March 2025; Accepted 21 July 2025; Issue published 31 August 2025

Abstract

Deep neural networks provide accurate results for most applications. However, they need a big dataset to train properly. Providing a big dataset is a significant challenge in most applications. Image augmentation refers to techniques that increase the amount of image data. Common operations for image augmentation include changes in illumination, rotation, contrast, size, viewing angle, and others. Recently, Generative Adversarial Networks (GANs) have been employed for image generation. However, like image augmentation methods, GAN approaches can only generate images that are similar to the original images. Therefore, they also cannot generate new classes of data. Texture images present more challenges than general images, and generating textures is more complex than creating other types of images. This study proposes a gradient-based deep neural network method that generates a new class of texture. It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks. After generating new textures for each class, the number of textures increases through image augmentation. During this process, several techniques are proposed to automatically remove incomplete and similar textures that are created. The proposed method is faster than some well-known generative networks by around 4 to 10 times. In addition, the quality of the generated textures surpasses that of these networks. The proposed method can generate textures that surpass those of some GANs and parametric models in certain image quality metrics. It can provide a big texture dataset to train deep networks. A new big texture dataset is created artificially using the proposed method. This dataset is approximately 2 GB in size and comprises 30,000 textures, each 150 × 150 pixels in size, organized into 600 classes. It is uploaded to the Kaggle site and Google Drive. This dataset is called BigTex. Compared to other texture datasets, the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.

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Keywords

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