Structure-sensitive manifold ranking for video concept detection

  • Authors:

  • Jinhui Tang;Xian-Sheng Hua;Guo-Jun Qi;Meng Wang;Tao Mei;Xiuqing Wu

  • Affiliations:

  • University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;University of Science and Technology of China, Hefei, China

  • Venue:
  • Proceedings of the 15th international conference on Multimedia
  • Year:
  • 2007

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Abstract

Pairwise similarity of samples is an essential factor in graph propagation based semi-supervised learning methods. Usually it is estimated based on Euclidean distance. However, the structural assumption, which is a basic assumption in these methods, has not been taken into consideration in the normal pairwise similarity measure. In this paper, we propose a novel graph-based learning approach, named Structure-Sensitive Manifold Ranking (SSMR),based on a structure-sensitive similarity measure. Instead of using distance only, SSMR takes local distribution differences into account to more accurately measure pairwise similarity. Furthermore, we show that SSMR can also be deduced from a partial differential equation based anisotropic diffusion. Experiments conducted on the TRECVID dataset show that this approach significantly outperforms existing graph-based semi-supervised learning methods for video semantic concept detection.