HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera

Alexander J. Medeiros, Lee Stearns, Jon E. Froehlich
11/20/2023
cs.CV

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Abstract

We demonstrate the use of DeCAF and Improved Fisher Vector image features to classify clothing texture. The issue of choosing clothes is a problem for the blind every day. This work attempts to solve the issue with a finger-mounted camera and state-of-the-art classification algorithms. To evaluate our solution, we collected 520 close-up images across 29 pieces of clothing. We contribute (1) the HCTD, an image dataset taken with a NanEyeGS camera, a camera small enough to be mounted on the finger, and (2) evaluations of state-of-the-art recognition algorithms applied to our dataset - achieving an accuracy >95%. Throughout the paper, we will discuss previous work, evaluate the current work, and finally, suggest the project's future direction.

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Cite this paper

@article{medeiros2023handsight,
  title  = {HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera},
  author = {Alexander J. Medeiros and Lee Stearns and Jon E. Froehlich},
  year   = {2023},
  eprint = {2311.12225},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.12225v1}
}

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