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Automatic image annotation using community detection in neighbor images

Author

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  • Maihami, Vafa
  • Yaghmaee, Farzin

Abstract

Automatic image annotation is useful in automatic image management and, understanding their contents. Automatic image annotation refers to the process of assigning tags/labels to images so that they clearly indicate the content which is meant to be conveyed. In this paper, a new method of automatic image annotation is presented. In the first phase of the proposed method, neighbor images which are similar to the query image, are retrieved using low-level features. In the second phase, a network (a relative graph) of the tags neighbor images is created and, finally, the tags of the densest community among all communities is selected for the query image as final tags. In order to show the efficiency of the proposed method some metrics such as precision, recall, and f-score are used in three standard datasets namely Corel5k, IAPR TC12 and Mir Flickr. The results show that the proposed method is more efficient than some state-of-the-art approaches.

Suggested Citation

  • Maihami, Vafa & Yaghmaee, Farzin, 2018. "Automatic image annotation using community detection in neighbor images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 123-132.
    • Handle: RePEc:eee:phsmap:v:507:y:2018:i:c:p:123-132
    DOI: 10.1016/j.physa.2018.05.028
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    3. Tasgin, Mursel & Bingol, Haluk O., 2018. "Community detection using preference networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 126-136.
    4. Chang, Zhenhai & Yin, Xianjun & Jia, Caiyan & Wang, Xiaoyang, 2018. "Mixture models with entropy regularization for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 339-350.
    5. Wang, Tao & Yin, Liyan & Wang, Xiaoxia, 2018. "A community detection method based on local similarity and degree clustering information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1344-1354.
    6. Zhou, Cangqi & Feng, Liang & Zhao, Qianchuan, 2018. "A novel community detection method in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1679-1693.
    7. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
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