| dc.description.abstract | © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Clustering tagged videos into semantic groups is important but challenging due to the need for jointly learning correlations between heterogeneous visual and tag data. The task is made more difficult by inherently sparse and incomplete tag labels. In this work, we develop a method for accurately clustering tagged videos based on a novel Hierarchical-Multi- Label Random Forest model capable of correlating structured visual and tag information. Specifically, our model exploits hierarchically structured tags of different abstractness of semantics and multiple tag statistical correlations, thus discovers more accurate semantic correlations among different video data, even with highly sparse/incomplete tags. | en_US |
