Tag refinement based on multilingual tag hierarchies extracted from image folksonomy
Shota Hamano, Takahiro Ogawa, Miki Haseyama · 2017
This paper presents a novel method for tag refinement using multilingual sources of tagged images in an image folksonomy. The proposed method enables accurate tag refinement by effectively leveraging multilingual sources of tags and considering the hierarchical structure of tags in the following way. First, synonymous tags across different languages are detected based on similarities between tagged images. In this stage, the proposed method utilizes visual similarities to effectively detect synonymous tags since the visual features extracted from images should be similar if they are assigned tags with the same meaning in different languages. Then hierarchical structure of the tags are extracted based on the similarity between the detected synonymous tags. The hierarchical structure provides hypernymous and hyponymous tags of the target tags, which are important for considering the relevance between tags and images. Consulting the hierarchical structure enables removal of irrelevant tags from the images and assignment of relevant tags to the images. The proposed method effectively utilizes tags in various languages in an image folksonomy. Experimental results show the effectiveness of introducing multilingual sources of tagged images for accuracy improvement in tag refinement.