Semantic video indexing by fusing explicit and implicit context spaces

  • Authors:

  • Yingbin Zheng;Renzhong Wei;Hong Lu;Xiangyang Xue

  • Affiliations:

  • Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China

  • Venue:
  • Proceedings of the international conference on Multimedia
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper addresses the problem of context-based concept fusion (CBCF) for concept detection and semantic video indexing. We introduce a novel framework based on constructing context spaces of concepts, such that the contextual correlations are used to improve the performance of concept detectors. Different from traditional CBCF approach, we present two kinds of such context spaces: explicit context space for modeling the correlation of pairwise concepts, and implicit context space for representing latent themes trained from a set of concepts. The final concept detection scores are then directly fused from explicit and implicit context spaces. Experiments are presented on TRECVid 2006 benchmark and the comparisons with several state-of-the-art approaches demonstrate the effectiveness of proposed framework.