Paper Info
Title
Automatic concept-to-query mapping for web-based concept detector training
Abstract
Nowadays, online platforms like YouTube provide massive content for training of visual concept detectors. However, it remains a difficult challenge to retrieve the right training content from such platforms since the underlying query construction can be arbitrarily complex. In this paper we present an approach, which offers an automatic concept-to-query mapping for training data acquisition from such platforms. Queries are automatically constructed by a keyword selection and a category assignment using ImageNet and Google Sets as external sources. Our results demonstrate that the proposed method is able to reach retrieval results comparable to queries constructed by humans providing 76% more relevant content for detector training than a one-to-one mapping of concept names to retrieval queries would do.
Year
DOI
Venue
2011
10.1145/2072298.2072038
ACM Multimedia 2001
Keywords
Field
DocType
web-based concept detector training,right training content,training data acquisition,one-to-one mapping,retrieval result,detector training,retrieval query,concept name,automatic concept-to-query mapping,massive content,relevant content,data acquisition,query expansion
Training set,Web search query,World Wide Web,Query language,Information retrieval,Query expansion,Computer science,Web query classification,Web application,Detector
Conference
Citations 
PageRank 
References 
3
0.39
18
Authors
3
Name
Order
Citations
PageRank
Damian Borth176449.45
Adrian Ulges232826.61
Thomas M. Breuel32362219.10