International Conference on Discovery Science

Discovery Science pp 33-47 | Cite as

Active Learning for Classifying Template Matches in Historical Maps

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9356)

Abstract

Historical maps are important sources of information for scholars of various disciplines. Many libraries are digitising their map collections as bitmap images, but for these collections to be most useful, there is a need for searchable metadata. Due to the heterogeneity of the images, metadata are mostly extracted by hand—if at all: many collections are so large that anything more than the most rudimentary metadata would require an infeasible amount of manual effort. We propose an active-learning approach to one of the practical problems in automatic metadata extraction from historical maps: locating occurrences of image elements such as text or place markers. For that, we combine template matching (to locate possible occurrences) with active learning (to efficiently determine a classification). Using this approach, we design a human computer interaction in which large numbers of elements on a map can be located reliably using little user effort. We experimentally demonstrate the effectiveness of this approach on real-world data.

Keywords

Active learning Threshold detection Human computer interaction Template matching Historical maps Knowledge discovery 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Chair of Computer Science IUniversität WürzburgWürzburgGermany

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