Similarity and duplicate search in artwork images

Type: MA thesis

Status: finished

Date: October 8, 2021 - April 7, 2022

Supervisors: Prathmesh Madhu, Ronak Kosti, Andreas Maier

Similarity and duplicate search in artworks is important in the domain of the art historical studies. It is a very
challenging task even for the art historians, because the image similarity in art depends on different features
like color, texture and style of the artwork [2]. Applying some pretrained deep neural networks like VGG16,
ResNet15 makes it efficient to find similar features between images. However, they have high bias on one of
the features (e.g., focus too much on color) and we usually do not know which features influence most on
similarity search, so they cannot be directly applied on artwork databases.

The goal of this thesis is to implement and evaluate a model based on deep neural networks that can find cor-
related artworks with a custom definition of similarity.