Image Explorer: Multi-Layered Touch Exploration to Make Images Accessible
Jaewook Lee, Yi-Hao Peng, Jaylin Herskovitz, Anhong Guo
Abstract
Blind or visually impaired (BVI) individuals often rely on alternative text (alt-text) in order to understand an image; however, alt-text is often missing or incomplete. Automatically-generated captions are a more scalable alternative, but they are also often missing crucial details, and, sometimes, are completely incorrect, which may still be falsely trusted by BVI users. We hypothesize that additional information could help BVI users better judge the correctness of an auto-generated caption. To achieve this, we present Image Explorer, a touch-based multi-layered image exploration system that enables users to explore the spatial layout and information hierarchies in an image. Image Explorer leverages several off-the-shelf deep learning models to generate segmentation and labeling results for an image, combines and filters the generated information, and presents the resulted information in hierarchical layers. In a pilot study with three BVI users, participants used Image Explorer, Seeing AI, and Facebook to explore images with auto-generated captions of diverging quality, and judge the correctness of the captions. Preliminary results show that participants made more accurate judgements about the correctness of the captions when using Image Explorer, although they were highly confident about their judgement regardless of the tool used. Overall, Image Explorer is a novel touch exploration system that makes images more accessible for BVI users by potentially encouraging skepticism and enabling users to independently validate auto-generated captions.