Tagging tagged images: on the impact of existing annotations on image tagging

Abstract

Crowdsourcing has been widely used to generate metadata for multimedia resources. By presenting partially described resources to human annotators, resources are tagged yielding better descriptions. Although significant improvements in metadata quality have been reported, as yet there is no understanding of how taggers are biased by previously acquired resource tags. We hypothesize that the number of existing annotations, which we take here to reflect the tag completeness degree, influence taggers: rather empty descriptions (initial tagging stages) encourage creating more tags, but better tags are created for fuller descriptions (later tagging stages). We explore empirically the relationship between tag quality/quantity and completeness degree by conducting a study on a set of human crowdsourcing annotators over a collection of images with different completeness degrees. Experimental results show a significant relation between completeness and image tagging. To the best of our knowledge, this study is the first to explore the impact of existing annotations on image tagging.