A Review of Image Retrieval Methods – a journey from image descriptors to neural networks
Although visual information is getting more and more common in the online sphere and researchers gave us plenty of tools to deal with it, it is still hard to find the right solution to the most common information retrieval tasks like finding duplicates, similar items and forming meaningful clusters from images. On a dataset with about 50k images we went through the traditional approaches like using image hashing and image descriptors for finding duplicates and clusters, we tried out image labeling solutions and we tested state-of-the-art variational autoencoders too. Of course, we compared and evaluated each and every solution and now we would like to share our experiences with you.
Research Lead, Precognox
Zoltan Varju is the Research Lead at Precognox. He’s running R&D projects for industry and academic partners in various fields from medical semantic search to content analysis. Zoltan is the founder of Hungarian Natural Language Processing Meetup and he’s a regular author of nyest.hu, a popular scientific online magazine devoted to language and science.