Content-Based Image Retrieval for Fabric Identification
Summary
Content-Based Image Retrieval (CBIR) for fabric identification employs computational analysis of visual characteristics of textiles—such as colour, texture and pattern—to enable precise querying and matching within large image databases. Traditional approaches to fabric classification relied on manual inspection and metadata tagging, which are labour intensive and prone to inconsistency. Advances in machine learning and computer vision have fostered automated systems that extract discriminative features directly from images, using methods ranging from handcrafted descriptors to deep neural networks. Practical applications span e-commerce—where customers may seek cloths with specific patterns—automated quality control in manufacturing, cultural heritage conservation and forensic textile analysis. Contemporary systems integrate feature extraction, similarity measurement and indexing strategies to balance retrieval accuracy with computational efficiency. The global textile industry, characterised by vast product diversity and rapid design iteration, stands to benefit from scalable CBIR platforms that support fine-grained fabric identification and visual search capabilities.
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Content-Based Image Retrieval for Fabric Identification publication trend
The graph below shows the total number of articles in content-based image retrieval for fabric identification across all publications each year (not limited to Nature Index journals).
Technical terms
Content-Based Image Retrieval (CBIR): A technique for searching image databases by analysing visual content rather than relying on metadata or manual tags.
Convolutional Neural Network (CNN): A deep learning architecture that applies convolutional filters to images for hierarchical feature extraction, widely used in image recognition tasks.
Hashing Learning: The process of converting high-dimensional feature vectors into compact binary codes to enable fast similarity search and reduced storage requirements.
Soft Similarity: A metric that quantifies fine-grained similarity between image pairs, allowing for nuanced distinctions beyond binary labels.
Mean Average Precision (mAP): A standard evaluation metric in retrieval systems, representing the average of precision values computed at different recall levels.
References
- Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural Network Method. Journal of Imaging (2023).
- Fabric image retrieval based on decoupling of texture and color feature. Journal of Engineered Fibers and Fabrics (2024).
- Mélange fabric image retrieval based on soft similarity learning. Journal of Engineered Fibers and Fabrics (2022).
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