The Hubble Image Similarity Project
We have created a large database of similarity information between subregions of Hubble Space Telescope images. These data can be used to assess the accuracy of image-search algorithms based on computer vision methods. The images were compared by humans in a citizen science project, where they were...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/adcb43 |
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| author | Richard L. White J. E. G. Peek |
| author_facet | Richard L. White J. E. G. Peek |
| author_sort | Richard L. White |
| collection | DOAJ |
| description | We have created a large database of similarity information between subregions of Hubble Space Telescope images. These data can be used to assess the accuracy of image-search algorithms based on computer vision methods. The images were compared by humans in a citizen science project, where they were asked to select similar images from a comparison sample. We utilized the Amazon Mechanical Turk system to pay our reviewers a fair wage for their work. Nearly 850,000 comparison measurements have been analyzed to construct a similarity distance matrix between all the pairs of images. We describe the algorithm used to extract a robust distance matrix from the (sometimes noisy) user reviews. The results are very impressive: The data capture similarity between images based on morphology, texture, and other details that are sometimes difficult even to describe in words (e.g., dusty absorption bands with sharp edges). The collective visual wisdom of our citizen scientists matches the accuracy of the trained eye, with even subtle differences among images faithfully reflected in the distances. |
| format | Article |
| id | doaj-art-cdfc6f4b8fbe4d5c8fad6b00af9a1f4e |
| institution | Kabale University |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-cdfc6f4b8fbe4d5c8fad6b00af9a1f4e2025-08-20T03:49:32ZengIOP PublishingThe Astronomical Journal1538-38812025-01-01169630610.3847/1538-3881/adcb43The Hubble Image Similarity ProjectRichard L. White0https://orcid.org/0000-0002-9194-2807J. E. G. Peek1https://orcid.org/0000-0003-4797-7030Space Telescope Science Institute , 3700 San Martin Drive, Baltimore, MD 21218, USA ; rlw@stsci.eduSpace Telescope Science Institute , 3700 San Martin Drive, Baltimore, MD 21218, USA ; rlw@stsci.edu; Department of Physics & Astronomy, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USAWe have created a large database of similarity information between subregions of Hubble Space Telescope images. These data can be used to assess the accuracy of image-search algorithms based on computer vision methods. The images were compared by humans in a citizen science project, where they were asked to select similar images from a comparison sample. We utilized the Amazon Mechanical Turk system to pay our reviewers a fair wage for their work. Nearly 850,000 comparison measurements have been analyzed to construct a similarity distance matrix between all the pairs of images. We describe the algorithm used to extract a robust distance matrix from the (sometimes noisy) user reviews. The results are very impressive: The data capture similarity between images based on morphology, texture, and other details that are sometimes difficult even to describe in words (e.g., dusty absorption bands with sharp edges). The collective visual wisdom of our citizen scientists matches the accuracy of the trained eye, with even subtle differences among images faithfully reflected in the distances.https://doi.org/10.3847/1538-3881/adcb43Astronomy image processingAstronomy data analysisAstronomy databasesGalaxiesNebulaeStar clusters |
| spellingShingle | Richard L. White J. E. G. Peek The Hubble Image Similarity Project The Astronomical Journal Astronomy image processing Astronomy data analysis Astronomy databases Galaxies Nebulae Star clusters |
| title | The Hubble Image Similarity Project |
| title_full | The Hubble Image Similarity Project |
| title_fullStr | The Hubble Image Similarity Project |
| title_full_unstemmed | The Hubble Image Similarity Project |
| title_short | The Hubble Image Similarity Project |
| title_sort | hubble image similarity project |
| topic | Astronomy image processing Astronomy data analysis Astronomy databases Galaxies Nebulae Star clusters |
| url | https://doi.org/10.3847/1538-3881/adcb43 |
| work_keys_str_mv | AT richardlwhite thehubbleimagesimilarityproject AT jegpeek thehubbleimagesimilarityproject AT richardlwhite hubbleimagesimilarityproject AT jegpeek hubbleimagesimilarityproject |