JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2016, Vol. 46 ›› Issue (5): 1-6.doi: 10.6040/j.issn.1672-3961.1.2015.316

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Image annotation refinement based on contextual graph diffusion

TIAN Feng1, LIU Zhuoxuan1, SHANG Fuhua1*, SHEN Xukun2, WANG Mei1, WANG Haochang1   

  1. 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, Heilongjiang, China;
    2. State Key Laboratory of Virtual Reality Technology and Systems, BeiHang University, Beijing 100191, China
  • Received:2015-03-01 Online:2016-10-20 Published:2015-03-01

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Abstract: A new image annotation refinement method was proposed. The initial labels were firstly obtained by annotation methods. Then label relevant graph and visual relevant graph were constructed and mutually reinforced. The semantic optimization problem was formulated into a regularized framework on above undirected weighted graphs. With strategies like data partitioning, successive optimization and constraint relaxation, an approximate optimized solution was got. The refined result could be more related to the content of images by incorporating both visual content and contextual information. Moreover, the proposed method was robust with the partition granularity, and the complexity was approximately linear. Experimental results on large scale web image dataset showed that the proposed method outperformed others, and could achieve both efficiency and capability.

Key words: contextual information diffusion, large scale data set, contextual graph, image annotation, annotation refinement

CLC Number: 

  • TP391
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