352-What to do with 2.000.000 Historical Press Photos? The Challenges and Opportunities of Applying a Scene Detection Algorithm to a Digitised Press Photo Collection

What to do with 2.000.000 Historical Press Photos? The Challenges and Opportunities of Applying a Scene Detection Algorithm to a Digitised Press Photo Collection

  1. https://www.tmgonline.nl/article/10.18146/tmg.815/
    In recent decades, historians have started to emphasise the importance of photographs as historical sources of evidence. Even though historians commonly draw from textual sources or oral testimony, Peter Burke argues that we should start taking images seriously as a means to learn about the past.1 Historical photographs and what is represented in them can be used to study specific world views, cultural-historical phenomena, or expressions of identity.2 Edwards and Hart examined the photograph’s materiality and traced the material forms in which images appeared.3 Others took a more contextual approach and examined how institutional forces determined how images were produced, distributed, and presented.4 A specific focus is on press photography and how this has shaped how we view images and read the news.5

  2. https://noord-hollandsarchief.nl/nieuws/nieuwsoverzicht/1154-historische-fotos-beter-doorzoekbaar-met-ai
    De getrainde computermodellen, documentatie en de volledige lijst met categorieën en hun omschrijving (Nederlands- en Engelstalig) is voor hergebruik beschikbaar op Zenodo en Github (pagina’s van Melvin Wevers):
    Het Noord-Hollands Archief stelt de gehele collectie van Fotopersbureau De Boer voor hergebruik beschikbaar met een Creative Commons Zero publieke domein licentie (CC0). Dit geldt ook voor de gebruikte beelden in de training. Bij gebruik van afbeeldingen of de dataset wordt bronvermelding wel zeer op prijs gesteld.

    Meer weten?
    Wil je meer weten over de inzet van scene detection en beeldherkenning in het algemeen? In dit artikel gaan we er dieper op in (open beschikbaar):

  3. https://velehanden.nl/

  4. hat did we learn from the pilots?
    In this project we investigated which type of objects or collections are well suited for automatic image recognition, how image recognition can be deployed in the process of collection registration and publication, and how AI can answer identification questions from the public. We developed image recognition models for a variety of objects from the collections of Naturalis, Museon-Omniversum, Het Natuurhistorisch and a handful of other institutions. These models can be used to identify specific object types or species. The project provided a number of learned lessons for the museum sector about how to develop and deploy image recognition models. Important lessons concern the requirements for the images and associated data that are needed to train models, the amount of training data needed for different types of objects and how to best define classes in your model.

    Public interface and additional information
    The public web interface launched in this project provides the user with the identifications generated by the different image recognition models and additional information about the species or object type (i.e. concise chunks of textual information, either with or without additional reference images). The additional information is both embedded in the interface itself as well as provided through links to existing additional online sources. With this information, users can verify the AI identifications with validated knowledge about the specific object type or species.The additional content was developed by Naturalis and project partners. Also, through collaboration with partners, we made good (re)use of content created within other ongoing projects. All content is sustainably managed in the information infrastructure of Naturalis and is suitable for reuse in other contexts.

    Symposium for and with the Dutch heritage sector
    We concluded the project with a symposium titled ‘Museum Collections & AI’ in Naturalis. During this symposium we shared the project results and lessons learned with colleagues from various museums, libraries and archives, with biodiversity researchers and with other people interested in the topic of AI. During this afternoon, the public web interface was launched: a website on which the developed recognition models can be used by both the public and heritage institutions. A number of other AI initiatives in the Dutch heritage sector were also discussed and presented during the symposium.

    We are very grateful to NLBIF, the Mondriaan Fonds and Prins Bernhard Cultuurfonds for their support. Thanks to their financial contribution, this project was able to come to fruition.

  5. https://zenodo.org/record/7050651/files/Naturalis%20Biodiversity%20Center%20%282022%29.%20Eindrapportage%20project%20Automatische%20beeldherkenning%20voor%20museumcollecties.pdf?download=1



  6. This repository provides Python code that identifies plants, birds, and insects in photos.

    This project was inspired by the amazing progress in identifying plants, animals and mushrooms in photos that has been made by iNaturalist in the past years. The iNaturalist team has trained machine learning models with their vast collection of photos and research-grade identifications. In 2019, iNaturalist released Seek by iNaturalist which identifies photos offline on the phone and identifies to a higher level than species when an identification to species cannot be made.

    Google provides three models that have been trained with iNaturalist data - classification models for plants, birds, and insects. These Google models can be downloaded and used with Google's TensorFlow and TensorFlow Lite tools.

    This code is based on the trained models that Google provides. It has been written to experiment with identification of species from photos and to give Seek's approach a try and compute probabilities across the taxonomic hierarchy.

    This tool nature_id.py has been tested on Linux and Windows. It likely works on macOS as well.

  7. https://www.youtube.com/watch?v=oE71KRItpOk
  8. https://www.youtube.com/watch?v=jpJmKkOMsGk
  9. https://www.natuurkennis.nl/publicaties-extra/rapporten-provincies/noord-brabant/
  10. https://landvanons.nl/wp-content/uploads/2021/04/Beheerplan-Onneresch-februari-2-2022.pdf

  11. Conversation Dragonflies Europe Biodiversity

  12. Fieldguide Damselfies New Guinea

  13. Fieldguide Damselfies Dragonflies

  14. Snelle toolBrachtron artikelen

  15. Atlas of the European dragonflies and damselflies

  16. https://ecuador.inaturalist.org/journal/ahospers/70169-335-download-italian-bird-migration-atlas
  17. https://paddenstoelenwerkgroepdrent.files.wordpress.com/2019/12/def-deel-1-paddenstoelenatlas-drenthe-hfd-1.pdf

  18. https://vlinderwerkgroepdrenthe.nl/dagvlinderatlas-op-deze-website-te-bekijken/
  19. https://ebba2.info/maps/
    Atlas Portugal, Hongarije, Zwitserland

Publicado el noviembre 5, 2022 12:58 TARDE por ahospers ahospers


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