A Hybrid Feature-based SVM Approach for Image-based Fabric Type Classification

Key Points

• The hybrid feature-based support vector machine approach classifies fabric types with enhanced accuracy.
• Classification accuracy improved by 15% using image data and advanced features extraction methods.
• Observational analysis employs a hybrid model integrating various image features for classification tasks.
• Highlights the potential of combining different methodologies to enhance fabric classification accuracy.

Abstract