Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary

February 14, 2018 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Meg Pirrung, Nathan Hilliard, ArtΓ«m Yankov, Nancy O'Brien, Paul Weidert, Courtney D Corley, Nathan O Hodas arXiv ID 1802.05316 Category cs.HC: Human-Computer Interaction Citations 13 Venue arXiv.org Last Checked 4 months ago
Abstract
Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user's mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the application's algorithmic underpinnings. Methods of interaction within Sharkzor's user interface and user experience support three primary user tasks; triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. After interacting with the images and groups, deep learning helps to automate the user's interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.
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