The application of graph diffusion in high-level feature extraction

Xiaohan Du; Honggang Zhang; Jun Guo; Xiaojun Xu

doi:10.1117/12.2013805

13 March 2013 The application of graph diffusion in high-level feature extraction

Xiaohan Du, Honggang Zhang, Jun Guo, Xiaojun Xu

Proceedings Volume 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing; 87830M (2013) https://doi.org/10.1117/12.2013805
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

In this paper, a new graph diffusion method is presented to improve the high-level feature extraction performance. In this method, we construct a semantic graph by describe the concepts as nodes and the concept affinities as the weights of edges, then we use the training set and its corresponding label matrix to estimate the concept relationship, where the relationship of two concepts were measured by the inner product of its corresponding row vector. We test the method on the high-level feature extraction task of TRECVID 2009 and the experimental results show the effectiveness of the method.

Citation Download Citation

Xiaohan Du, Honggang Zhang, Jun Guo, and Xiaojun Xu "The application of graph diffusion in high-level feature extraction", Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830M (13 March 2013); https://doi.org/10.1117/12.2013805

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KEYWORDS

Feature extraction

Diffusion

Video

Semantic video

Sensors

Image fusion

Data modeling

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