Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolution
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.
implicit crowdsourcing visual concept modeling user feedback clickthrough data automatic image annotation