Social Tag Enrichment via Automatic Abstract Tag Refinement

Zhaoqiang Xia, Jinye Peng, Xiaoyi Feng, Jianping Fan

Abstract: Collaborative image tagging systems, such as Flickr, are very attractive for supporting keyword-based image retrieval, but some social tags of these collaboratively-tagged social images might be imprecise. Some people may use general or high-level words (i.e., abstract tags) to tag their images for saving time and effort, thus such general or high-level tags are too abstract to describe the visual content of social images precisely. As a result, users may not be able to find what they need when they use the specific keywords for query specification. To tackle this problem of abstract tags, a concept ontology is constructed for detecting the abstract tags from large-scale social images. The co-occurrence contexts of social tags and k-NN algorithm with Gaussian Weight are used to find the most specific tags which can signify out the abstract tags. In addition, all the relevant keywords, which are corresponded with intermediate nodes between the high-level concepts (abstract tags) and object classes (most specific tags) on our concept ontology, are added to enrich the lists of social tags, so that users can have more choices to select various keywords for query specification. We have tested our proposed algorithms on two data sets with different images.