Semantic annotation of personal video content using an image folksonomy

Authors:
Hyun-Seok Min;JaeYoung Choi;Wesley De Neve;Yong Man Ro;Konstantinos N. Plataniotis
Affiliations:
Image and Video Systems Lab, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Korea;Image and Video Systems Lab, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Korea;Image and Video Systems Lab, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Korea;Image and Video Systems Lab, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon, Korea;Multimedia Lab, The Edward S. Rogers Sr. Dept. of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

The increasing popularity of user-generated content (UGC) requires effective annotation techniques in order to facilitate precise content search and retrieval. In this paper, we propose a new approach for the semantic annotation of personal video content, taking advantage of user-contributed tags available in an image folksonomy. Video shots and folksonomy images are first represented by a semantic vector. Next, the semantic vectors are used to measure the semantic similarity between each video shot and the folksonomy images. Tags assigned to semantically similar folksonomy images are then used to annotate the video shots. To verify the effectiveness of the proposed annotation method, experiments were performed with video sequences retrieved from YouTube and images downloaded from Flickr. Our experimental results demonstrate that the proposed method is able to successfully annotate personal video content with user-contributed tags retrieved from an image folksonomy. In addition, the size of our tag vocabulary is significantly higher than the size of the tag vocabulary used by conventional annotation methods.