Interactive Mars Image Content-Based Search with Interpretable Machine Learning
<|reference_start|>Interactive Mars Image Content-Based Search with Interpretable Machine Learning: The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used by a classifier trained on images from the Mars Science Laboratory (MSL) Curiosity rover mission. In addition to providing explanations, we investigate the diversity and correctness of evidence used by the content-based classifier. The work presented in this paper will be deployed on the PDS Image Atlas, replacing its non-interpretable counterpart.<|reference_end|>
@article{vasu2024interactive,
    title={Interactive Mars Image Content-Based Search with Interpretable Machine
  Learning},
    author={Bhavan Vasu, Steven Lu, Emily Dunkel, Kiri L. Wagstaff, Kevin Grimes,
  Michael McAuley},
    journal={arXiv preprint arXiv:2402.16860},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16860},
    primaryClass={cs.CV cs.IR}
}