Interactive Mars Image Content-Based Search with Interpretable Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911671933468672 |
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| author | Vasu, Bhavan Lu, Steven Dunkel, Emily Wagstaff, Kiri L. Grimes, Kevin McAuley, Michael |
| author_facet | Vasu, Bhavan Lu, Steven Dunkel, Emily Wagstaff, Kiri L. Grimes, Kevin McAuley, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interactive Mars Image Content-Based Search with Interpretable Machine Learning Vasu, Bhavan Lu, Steven Dunkel, Emily Wagstaff, Kiri L. Grimes, Kevin McAuley, Michael Computer Vision and Pattern Recognition Information Retrieval 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. |
| title | Interactive Mars Image Content-Based Search with Interpretable Machine Learning |
| topic | Computer Vision and Pattern Recognition Information Retrieval |
| url | https://arxiv.org/abs/2402.16860 |