Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
Automatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. It is a challenging texture classification problem in that ev...
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MDPI AG
2024-11-01
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| author | Reham Al-Majed Muhammad Hussain |
| author_facet | Reham Al-Majed Muhammad Hussain |
| author_sort | Reham Al-Majed |
| collection | DOAJ |
| description | Automatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. It is a challenging texture classification problem in that even images of the same texture class expose a high degree of intraclass variations. Moreover, images of clothes patterns may be taken in an unconstrained illumination environment. Machine learning methods proposed for this problem mostly rely on handcrafted features and traditional classification methods. The research works that utilize the deep learning approach result in poor recognition performance. We propose a deep learning method based on an ensemble of convolutional neural networks where feature engineering is not required while extracting robust local and global features of clothes patterns. The ensemble classifier employs a pre-trained ResNet50 with a non-local (NL) block, a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block as base learners. To fuse the individual decisions of the base learners, we introduce a simple and effective fusing technique based on entropy voting, which incorporates the uncertainties in the decisions of base learners. We validate the proposed method on benchmark datasets for clothes patterns that have six categories: solid, striped, checkered, dotted, zigzag, and floral. The proposed method achieves promising results for limited computational and data resources. In terms of accuracy, it achieves 98.18% for the GoogleClothingDataset and 96.03% for the CCYN dataset. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-6767adde2c064ca08f1c8823c55373ae2025-08-20T02:08:12ZengMDPI AGApplied Sciences2076-34172024-11-0114221073010.3390/app142210730Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern RecognitionReham Al-Majed0Muhammad Hussain1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaAutomatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. It is a challenging texture classification problem in that even images of the same texture class expose a high degree of intraclass variations. Moreover, images of clothes patterns may be taken in an unconstrained illumination environment. Machine learning methods proposed for this problem mostly rely on handcrafted features and traditional classification methods. The research works that utilize the deep learning approach result in poor recognition performance. We propose a deep learning method based on an ensemble of convolutional neural networks where feature engineering is not required while extracting robust local and global features of clothes patterns. The ensemble classifier employs a pre-trained ResNet50 with a non-local (NL) block, a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block as base learners. To fuse the individual decisions of the base learners, we introduce a simple and effective fusing technique based on entropy voting, which incorporates the uncertainties in the decisions of base learners. We validate the proposed method on benchmark datasets for clothes patterns that have six categories: solid, striped, checkered, dotted, zigzag, and floral. The proposed method achieves promising results for limited computational and data resources. In terms of accuracy, it achieves 98.18% for the GoogleClothingDataset and 96.03% for the CCYN dataset.https://www.mdpi.com/2076-3417/14/22/10730texture recognitionclothes patterndeep learningCNNensemble learningentropy |
| spellingShingle | Reham Al-Majed Muhammad Hussain Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition Applied Sciences texture recognition clothes pattern deep learning CNN ensemble learning entropy |
| title | Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition |
| title_full | Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition |
| title_fullStr | Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition |
| title_full_unstemmed | Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition |
| title_short | Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition |
| title_sort | entropy based ensemble of convolutional neural networks for clothes texture pattern recognition |
| topic | texture recognition clothes pattern deep learning CNN ensemble learning entropy |
| url | https://www.mdpi.com/2076-3417/14/22/10730 |
| work_keys_str_mv | AT rehamalmajed entropybasedensembleofconvolutionalneuralnetworksforclothestexturepatternrecognition AT muhammadhussain entropybasedensembleofconvolutionalneuralnetworksforclothestexturepatternrecognition |