Edinburgh Research Explorer

In Search of Art

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2014 Workshops
Subtitle of host publicationZurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part I
EditorsLourdes Agapito, Michael M. Bronstein, Carsten Rother
Place of PublicationCham
PublisherSpringer International Publishing
Pages54-70
Number of pages17
ISBN (Electronic)978-3-319-16178-5
ISBN (Print)978-3-319-16177-8
DOIs
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume8925
ISSN (Print)0302-9743

Abstract

The objective of this work is to find objects in paintings by learning object-category classifiers from available sources of natural images. Finding such objects is of much benefit to the art history community as well as being a challenging problem in large-scale retrieval and domain adaptation.

We make the following contributions: (i) we show that object classifiers, learnt using Convolutional Neural Networks (CNNs) features computed from various natural image sources, can retrieve paintings containing these objects with great success; (ii) we develop a system that can learn object classifiers on-the-fly from Google images and use these to find a large variety of previously unfound objects in a dataset of 210,000 paintings; (iii) we combine object classifiers and detectors to align objects to allow for direct comparison; for example to illustrate how they have varied over time.

ID: 28430773