การศึกษาเปรียบเทียบระหว่างวิธีการหาคุณลักษณะเฉพาะพื้นที่และวิธีการเรียนรู้เชิงลึก สำหรับการค้นคืนรูปภาพลายผ้าไหม

This paper aims to do a comparative study of local feature descriptor techniques and convolutional neural networks (CNN) for retrieving Thai silk pattern images. Two feature descriptor techniques, the histogram of oriented gradients and the scale-invariant feature transform, are compared to extract feature vectors from the silk pattern images. We combined the feature vectors extracted from feature descriptor techniques with k-nearest neighbors (KNN) and support vector machine. Then we modified CNN architectures: LeNet-5 and AlexNet. The LeNet-5 was modified by increasing the number of neurons in each layer of the fully connected layers. The AlexNet architecture was modified by reducing the neurons in each layer of the fully connected layers. Finally, we evaluated the local descriptor techniques, the existing CNN architectures and our modified CNN architectures on Thai silk pattern dataset. The results of the study showed that the local descriptor techniques combined with KNN algorithm significantly outperform the CNN methods. [ABSTRACT FROM AUTHOR]