Linked Open Images: Visual Similarity for the Semantic Web

Submitted by Lukas Klic on 09/14/2021 - 00:59

Tracking #: 2893-4107

Authors: 
Lukas Klic

Responsible editor: 
Special Issue Cultural Heritage 2021

Submission type: 
Application Report
Abstract: 
This paper presents ArtVision, a Semantic Web application that integrates computer vision APIs with the ResearchSpace platform, allowing for the matching of similar artworks and photographs across cultural heritage image collections. The field of Digital Art History stands to benefit a great deal from computer vision, as numerous projects have already made good progress in tackling issues of visual similarity, artwork classification, style detection, gesture analysis, among others. Pharos, the International Consortium of Photo Archives, is building its platform using the ResearchSpace knowledge system, an open-source semantic web platform that allows heritage institutions to publish and enrich collections as Linked Open Data through the CIDOC-CRM, and other ontologies. Using the images and artwork data of Pharos collections, this paper outlines the methodologies used to integrate visual similarity data from a number of computer vision APIs, allowing users to discover similar artworks and generate canonical URIs for each artwork.
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Previous Version: 
Linked Open Images: Visual Similarity for the Semantic Web
Tags: 
Reviewed

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
Anonymous submitted on 07/Oct/2021
Suggestion:
Accept
Review Comment:

All the comments I had raised have been addressed in the resubmitted version of the manuscript. I suggest to accept the paper in its present form.

Review #2
By Ronald Siebes submitted on 13/Oct/2021
Suggestion:
Accept
Review Comment:

In my initial review i've recommended the paper to be accepted under minor revision, which were:
The paper has a good structure, and easy to read. The code on GitHub looks mature, but
1: the GitHub 'readme' files and instructions for the website (https://vision.artresearch.net/resource/start) are too minimal.
2: the publicly available SPARQL endpoint and the REST API are missing in the paper.
These two points make it a 'minor revision' instead of accept.

The authors addressed both points very well by having now a sparql endpoint available
(https://vision.artresearch.net/sparql)
and better instructions both on GitHub (https://github.com/ArtResearch/vision-api) and in the about section (https://vision.artresearch.net/resource/?uri=Default%3AAbout)

Review #3
Anonymous submitted on 14/Oct/2021
Suggestion:
Accept
Review Comment:

The script has been improved based on my comments. One last thing would be that authors check for typos, for example, I see few occurrences of "the the", there are more such typos. Also, it would be good to run a final grammar check before submitting the final version.