HandSight: DeCAF & Improved Fisher Vectors to Classify Clothing Color and Texture with a Finger-Mounted Camera
This addresses the daily challenge of choosing clothes for blind people, though it appears incremental in applying existing methods to a new domain.
The paper tackles the problem of clothing texture classification for blind individuals using a finger-mounted camera, achieving over 95% accuracy on a dataset of 520 close-up images across 29 clothing items.
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.