Improving object detection in paintings based on time contexts

Maria Cristina Marinescu, Artem Reshetnikov, Joaquim More Lopez

Research output: Book chapterConference contributionpeer-review

4 Citations (Scopus)

Abstract

This paper proposes a novel approach to object detection for the Cultural Heritage domain, which relies on combining Deep Learning and semantic metadata about candidate objects extracted from existing sources such as Wikidata, dictionaries, or Google NGram. Working with cultural heritage presents challenges not present in every-day images. In computer vision, object detection models are usually trained with datasets whose classes are not imaginary concepts, and have neither symbolic nor time-specific dimensions. Apart from this conceptual problem, the paintings are limited in number and represent the same concept in potentially very different styles. Finally, the metadata associated with the images is often poor or inexistent, which makes it hard to properly train a model. Our approach can improve the precision of object detection by placing the classes detected by a neural network model in time, based on the dates of their first known use. By taking into account the time of inception of objects such as the TV, cell phone, or scissors, and the appearance of some objects in the geographical space that corresponds to a painting (e.g. bananas or broccoli in 15th century Europe), we can correct and refine the detected objects based on their chronologic probability.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages926-932
Number of pages7
ISBN (Electronic)9781728190129
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: 17 Nov 202020 Nov 2020

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Country/TerritoryItaly
CityVirtual, Sorrento
Period17/11/2020/11/20

Keywords

  • Computer Vision
  • Cultural Heritage
  • Deep Learning
  • Object Detection

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