Semi-supervised kernel density estimation for video annotation

  • Authors:

  • Meng Wang;Xian-Sheng Hua;Tao Mei;Richang Hong;Guojun Qi;Yan Song;Li-Rong Dai

  • Affiliations:

  • Microsoft Research Asia, Zhichun Road, Beijing 100080, PR China;Microsoft Research Asia, Zhichun Road, Beijing 100080, PR China;Microsoft Research Asia, Zhichun Road, Beijing 100080, PR China;University of Science and Technology of China, Huanshan Road, Hefei 230027, PR China;University of Science and Technology of China, Huanshan Road, Hefei 230027, PR China;University of Science and Technology of China, Huanshan Road, Hefei 230027, PR China;University of Science and Technology of China, Huanshan Road, Hefei 230027, PR China

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2009

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Abstract

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in large-scale video annotation due to several difficulties, such as large variation of video content and intractable computational cost. In this paper, we propose a novel semi-supervised learning algorithm named semi-supervised kernel density estimation (SSKDE) which is developed based on kernel density estimation (KDE) approach. While only labeled data are utilized in classical KDE, in SSKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. It is a non-parametric method, and it thus naturally avoids the model assumption problem that exists in many parametric semi-supervised methods. Meanwhile, it can be implemented with an efficient iterative solution process. So, this method is appropriate for video annotation. Furthermore, motivated by existing adaptive KDE approach, we propose an improved algorithm named semi-supervised adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels rather than a fixed kernel, such that broader kernels can be applied in the regions with low density. In this way, more accurate density estimates can be obtained. Extensive experiments have demonstrated the effectiveness of the proposed methods.