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About Batik Classification Using Deep Convolutional Netw
1. Batik fabric is an important part of Indonesian cultural heritage, and automatic classification of batik patterns could help preserve it. 2. Previous research on batik classification focused on handcrafted feature extraction methods like SIFT and SURF, which achieved 88-89% accuracy but had issues with invariance to transformations like rotation and scaling. 3. The paper proposes using a convolutional neural network (CNN) model with transfer learning, using VGG16 features extracted from batik images, to improve classification performance especially for rotated and scaled images.
Detailed Information
| Author: | ['Nara'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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