Abstract
Community tagging offers valuable information for media search and retrieval, but new media items are at a disadvantage. Automated tagging may populate media items with few tags, thus enabling their inclusion into search results. In this paper, a multi-label decision tree is proposed and applied to the problem of automated tagging of media data. In addition to binary labels, the proposed Iterative Split Multi-label Decision Tree (IS-MLT) is easily extended to the problem of weighted labels (such as those depicted by tag clouds). Several datasets of differing media types show the effectiveness of the proposed method relative to other multi-label and single label classifier methods and demonstrate its scalability relative to single label approaches.
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