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\begin{document}

\title{Interactive Deep Learning for Exploratory Sorting of PlantImages by
Visual Phenotypes}



\author[1,1]{Huimin Han}%
\author[1,1]{Ritvik Prabhu}%
\author[2,2]{Timothy Smith}%
\author[1,1]{Kshitiz Dhakal}%
\author[1,1]{Xing Wei}%
\author[1,1]{Song Li}%
\author[1,1]{Chris North}%
\affil[1]{Virginia Tech}%
\affil[2]{Virginia Techh}%


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  \date{May 26, 2024}


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\begin{abstract}
This paper proposes an interactive system called Andromeda1that enables
users to interact with machine learning models to allow for exploratory
sorting of images through a cognitive approach that uses a reduced
dimension plot. In our system, a dimension reduction algorithm projects
the images into a 2D space representing similarities between the images
based on visual features extracted by a deep neural network. With
Andromeda, users can alter the projection by dragging a subset of the
images into groups according to their domain expertise. The underlying
machine learning model learns the new projection by optimizing a
weighted distance function in the feature space, and the model
re-projects the images accordingly. The users can explore multiple
custom projections to learn about the visual support for different
groupings based on explainable-AI feedback. Our approach incorporates
user preferences into machine learning model construction and allows
transfer learning from pre-trained image processing models to accomplish
new tasks based on user inputs. Using edamame pod images as an example,
we interactively re-project the images into different groupings based on
maturity and disease, and identify important visual features from the
pixels highlighted by the model.%
\end{abstract}%



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