Kernel Matrix Based Textile Image Categorization Using Machine Learning
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Keywords:
Textile Images, Support Vector Machines, Multi-Layer Perceptron, K-Nearest Neighbor, Kernel MatrixAbstract
Today, just as text and audio data are rapidly increasing, visual data is also growing at a fast
pace. In textile technology, fabric patterns, and images have encountered a similar expansion.
Consequently, resolving this large volume of data becomes a significant problem. We need efficient
classification to quickly access this data, and this categorization process should be automated using
machine learning techniques. In our study, we have constructed the systems for classifying textile images
using machine learning methods such as Multilayer Perceptron, Support Vector Machines, and K-Nearest
Neighbors. The textile dataset consists of color and grayscale images, divided into training and test sets.
Models are trained using the training data, and their decision-making performance is evaluated using the
test data. During model generation, preprocessing is performed first. All images are converted to black
and white. Edge detection filters like Sobel and Prewitt are applied to find the edges in the images.
Optionally, thinning can also be applied before this step. After preprocessing, feature extraction is carried
out. For each image, the frequency of matrices called kernel matrices, which slide over the image, is
calculated and normalized. This representation allows images to be transformed into vectors, which are
then used to train machine learning models. In the testing phase, commonly used metrics such as F-score
and Accuracy are employed to evaluate the performance of these systems. The developed models are
compared to each other, and the most successful methods are determined.
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