Project Description

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. We initially demonstrated the feasibility of this approach using a small, highly-controlled dataset and combining two image classification techniques commonly used for object recognition. More recently, 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.


Applying Transfer Learning to Recognize Clothing Patterns Using a Finger-Mounted Camera

Lee Stearns, Leah Findlater, Jon E. Froehlich

Extended Abstract Proceedings of ASSETS 2018 | Acceptance Rate: 58.3% (42 / 72)

Recognizing Clothing Colors and Visual Textures Using a Finger-Mounted Camera: An Initial Investigation

Alexander Medeiros, Lee Stearns, Leah Findlater, Chuan Chen, Jon E. Froehlich

Extended Abstract Proceedings of ASSETS 2017