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.Keywords: Automated Multimedia Tagging; Community Tagging; Multi-label Classification; Multi-label Decision Tree; Pattern Classification
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Multimedia Data Engineering and Management
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
