Fun with visual similarity search – Content-based image retrieval (CBIR) with Neural Networks (EN)

Fun with visual similarity search – Content-based image retrieval (CBIR) with Neural Networks (EN)

11:15 - 11:45 | festival.stage | Start-up

Content-based image retrieval (CBIR), […] is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases […].“ – https://en.wikipedia.org/wiki/Content-based_image_retrieval

CBIR is widely used and is gaining popularity. With it you can search for cute dog pictures, find similar products or art , and even more. Up until recently CBIR used „classic computer vision“ algorithms, but neural networks are gaining traction.

This talk consists of two parts:

1. A *brief introduction to CBIR with neural networks*: the general setup and architecture, feature representations, similarity measures, metrics and losses, datasets, different ways of training, common problems, and differences to classic computer vision algorithms.

2. *Lessons learned*: how RoomAR uses CBIR, hands-on implementation of CBIR with PyTorch, speeding up the search, and some (fun) ideas of what you can do with this setup.

Stefan Otte, Chief Data Scientist, RoomAR