Effective annotation and search for video blogs with integration of context and content analysis

  • Authors:

  • Xiaoyu Zhang;Changsheng Xu;Jian Cheng;Hanqing Lu;Songde Ma

  • Affiliations:

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China and China-Singapore Institute of Digital Media, Singapore;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China and China-Singapore Institute of Digital Media, Singapore;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China and China-Singapore Institute of Digital Media, Singapore;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China and China-Singapore Institute of Digital Media, Singapore;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • IEEE Transactions on Multimedia - Special issue on integration of context and content
  • Year:
  • 2009

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Abstract

In recent years, weblogs (or blogs) have received great popularity worldwide, among which video blogs (or vlogs) are playing an increasingly important role. However, research on vlog analysis is still in the early stage, and how to manage vlogs effectively so that they can be more easily accessible is a challenging problem. In this paper, we propose a novel vlog management model which is comprised of automatic vlog annotation and user-oriented vlog search. For vlog annotation, we extract informative keywords from both the target vlog itself and relevant external resources; besides semantic annotation, we perform sentiment analysis on comments to obtain the overall evaluation. For vlog search, we present saliency-based matching to simulate human perception of similarity, and organize the results by personalized ranking and category-based clustering. An evaluation criterion is also proposed for vlog annotation, which assigns a score to an annotation according to its accuracy and completeness in representing the vlog's semantics. Experimental results demonstrate the effectiveness of the proposed management model for vlogs.