Images, Words, and Imagination: Accessible Descriptions to Support Blind and Low Vision Art Exploration and Engagement

Author:

Doore Stacy A.1ORCID,Istrati David1,Xu Chenchang2,Qiu Yixuan3,Sarrazin Anais4,Giudice Nicholas A.5ORCID

Affiliation:

1. INSITE Lab, Department of Computer Science, Colby College, Waterville, ME 04901, USA

2. Department of Computer Science, Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA

3. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA

4. Sonos, San Francisco, CA 94109, USA

5. VEMI Lab, School of Computing and Information Science, University of Maine, Orono, ME 04469, USA

Abstract

The lack of accessible information conveyed by descriptions of art images presents significant barriers for people with blindness and low vision (BLV) to engage with visual artwork. Most museums are not able to easily provide accessible image descriptions for BLV visitors to build a mental representation of artwork due to vastness of collections, limitations of curator training, and current measures for what constitutes effective automated captions. This paper reports on the results of two studies investigating the types of information that should be included to provide high-quality accessible artwork descriptions based on input from BLV description evaluators. We report on: (1) a qualitative study asking BLV participants for their preferences for layered description characteristics; and (2) an evaluation of several current models for image captioning as applied to an artwork image dataset. We then provide recommendations for researchers working on accessible image captioning and museum engagement applications through a focus on spatial information access strategies.

Funder

NSF

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

Reference77 articles.

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2. Garcia, N., and Vogiatzis, G. (2018, January 8–14). How to read paintings: Semantic art understanding with multi-modal retrieval. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.

3. (2022, October 02). Web Gallery of Art. Available online: https://www.wga.hu/.

4. Axel, E.S., and Levent, N.S. (2002). Art beyond Sight: A Resource Guide to Art, Creativity, and Visual Impairment, American Foundation for the Blind.

5. The Talking Museum Project;Amato;Procedia Comput. Sci.,2013

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