Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolution
Journal
Multimedia Tools and Applications
Date Issued
March 2014
DOI
10.1007/s11042-012-0995-2
Abstract
In this paper a novel approach for automatically annotating image databases is
proposed. Despite most current schemes that are just based on spatial content analysis, the
proposed method properly combines several innovative modules for semantically annotating
images. In particular it includes: (a) a GWAP-oriented interface for optimized collection of
implicit crowdsourcing data, (b) a new unsupervised visual concept modeling algorithm for
content description and (c) a hierarchical visual content display method for easy data
navigation, based on graph partitioning. The proposed scheme can be easily adopted by
any multimedia search engine, providing an intelligent way to even annotate completely
non-annotated content or correct wrongly annotated images. The proposed approach currently
provides very interesting results in limited-size both standard and generic datasets and
it is expected to add significant value especially to billions of non-annotated images existing
in the Web. Furthermore expert annotators can gain important knowledge relevant to user
new trends, language idioms and styles of searching.
proposed. Despite most current schemes that are just based on spatial content analysis, the
proposed method properly combines several innovative modules for semantically annotating
images. In particular it includes: (a) a GWAP-oriented interface for optimized collection of
implicit crowdsourcing data, (b) a new unsupervised visual concept modeling algorithm for
content description and (c) a hierarchical visual content display method for easy data
navigation, based on graph partitioning. The proposed scheme can be easily adopted by
any multimedia search engine, providing an intelligent way to even annotate completely
non-annotated content or correct wrongly annotated images. The proposed approach currently
provides very interesting results in limited-size both standard and generic datasets and
it is expected to add significant value especially to billions of non-annotated images existing
in the Web. Furthermore expert annotators can gain important knowledge relevant to user
new trends, language idioms and styles of searching.