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Author Funkhouser, Thomas ♦ Shin, Hijung ♦ Toler-Franklin, Corey ♦ Castaeda, Antonio Garca ♦ Brown, Benedict ♦ Dobkin, David ♦ Rusinkiewicz, Szymon ♦ Weyrich, Tim
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Shape matching ♦ Cultural heritage computer-assisted fresco reconstruction ♦ Machine learning
Abstract One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.
ISSN 15564673
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-11-01
Publisher Place New York
e-ISSN 15564711
Journal Journal on Computing and Cultural Heritage (JOCCH)
Volume Number 4
Issue Number 2
Page Count 13
Starting Page 1
Ending Page 13

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Source: ACM Digital Library