  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.
