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Multimedia Tools and Applications

, Volume 74, Issue 2, pp 595–611 | Cite as

Polysemious visual representation based on feature aggregation for large scale image applications

  • Xinghang SongEmail author
  • Shuqiang JiangEmail author
  • Shuhui Wang
  • Liang Li
  • Qingming Huang
Article

Abstract

Multiple image features and multiple semantic concepts from the images have intrinsic and complex relations. These relations influence the effectiveness of image semantic analysis methods, especially on the large scale problems. In this paper, a framework of generating polysemious image representation through three levels of feature aggregation is proposed. In the codebook level aggregation, visual dictionaries are learned for each feature type, and each image feature can be reconstructed with this dictionary. In the semantic level aggregation, the multiple concept distributions are learned with each feature codebook by using the improved local anchor embedding. Then the polysemious representation for for single feature type can be established after this level. In the multiple feature level aggregation, final image polysemious representation is obtained through multiple feature fusion with a weighted pooling approach. Through the proposed framework, multiple feature fusion and multiple semantic descriptions are both achieved in an integrated way. Experimental evaluations on large scale image dataset validate the effectiveness of the proposed method.

Keywords

Polysemious representation Feature aggregation Max pooling Large scale 

Notes

Acknowledgments

This work was supported in part by National Basic Research Program of China (973 Program):2012CB316400, in part by National Natural Science Foundation of China: 61322212, 61025011, 61332016 in part by the Key Technologies R&D Program of China:2012BAH18B02 and in part by in part by National Hi-Tech Development Program (863 Program) of China: 2014AA015202.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology(ICT)BeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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