NDLI: People with Visual Impairment Training Personal Object Recognizers: Feasibility and Challenges

Content Provider	ACM Digital Library
Author	Kacorri, Hernisa Kitani, Kris M. Bigham, Jeffrey P. Asakawa, Chieko
Abstract	Blind people often need to identify objects around them, from packages of food to items of clothing. Automatic object recognition continues to provide limited assistance in such tasks because models tend to be trained on images taken by sighted people with different background clutter, scale, viewpoints, occlusion, and image quality than in photos taken by blind users. We explore personal object recognizers, where visually impaired people train a mobile application with a few snapshots of objects of interest and provide custom labels. We adopt transfer learning with a deep learning system for user-defined multi-label k-instance classification. Experiments with blind participants demonstrate the feasibility of our approach, which reaches accuracies over 90% for some participants. We analyze user data and feedback to explore effects of sample size, photo-quality variance, and object shape; and contrast models trained on photos by blind participants to those by sighted participants and generic recognizers.
Starting Page	5839
Ending Page	5849
Page Count	11
File Format	PDF
ISBN	9781450346559
DOI	10.1145/3025453.3025899
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Blind Computer vision Accessibility Photographs Object recognition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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People with Visual Impairment Training Personal Object Recognizers: Feasibility and Challenges