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We propose an automatic pipeline for organizing, ranking, and suggesting the best images in a user’s

gallery based on computer vision methods on constrained devices. In contrast to most present solutions,

our current implementation clusters the images using a hierarchical approach based on three levels of

information (GPS location, datetime and image content) and suggests the best images based on

aesthetical and technical scores using a hybrid network. This compact network shares the same backbone

for the scores prediction while also being used to extract features from the images, which are then used

to clustering. This makes the solution lightweight, favoring constrained devices, while also enabling

running the entire pipeline locally, ensuring the user’s privacy.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.