ABSTRACT
Color identification tools do not identify visual patterns or allow users to quickly inspect multiple locations, which are both important for identifying clothing. We are exploring the use of a finger-based camera that allows users to query clothing colors and patterns by touch. Previously, we demonstrated the feasibility of this approach using a small, highly-controlled dataset and combining two image classification techniques commonly used for object recognition. Here, to improve scalability and robustness, we collect a dataset of fabric images from online sources and apply transfer learning to train an end-to-end deep neural network to recognize visual patterns. This new approach achieves 92% accuracy in a general case and 97% when tuned for images from a finger-mounted camera.
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Index Terms
- Applying Transfer Learning to Recognize Clothing Patterns Using a Finger-Mounted Camera
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