KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권9호
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  • Pages.4476-4490
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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KNN-based Image Annotation by Collectively Mining Visual and Semantic Similarities

  • Ji, Qian (School of Computer Science and Engineering, Nanjing University of Science and Technology) ;
  • Zhang, Liyan (School of Computer Science, Nanjing University of Aeronautics and Astronautics) ;
  • Li, Zechao (School of Computer Science and Engineering, Nanjing University of Science and Technology)
  • 투고 : 2016.11.26
  • 심사 : 2017.05.28
  • 발행 : 2017.09.30
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초록

The aim of image annotation is to determine labels that can accurately describe the semantic information of images. Many approaches have been proposed to automate the image annotation task while achieving good performance. However, in most cases, the semantic similarities of images are ignored. Towards this end, we propose a novel Visual-Semantic Nearest Neighbor (VS-KNN) method by collectively exploring visual and semantic similarities for image annotation. First, for each label, visual nearest neighbors of a given test image are constructed from training images associated with this label. Second, each neighboring subset is determined by mining the semantic similarity and the visual similarity. Finally, the relevance between the images and labels is determined based on maximum a posteriori estimation. Extensive experiments were conducted using three widely used image datasets. The experimental results show the effectiveness of the proposed method in comparison with state-of-the-arts methods.

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피인용 문헌

  1. A review on visual content-based and users’ tags-based image annotation: methods and techniques vol.79, pp.29, 2017, https://doi.org/10.1007/s11042-020-08862-1
  2. Image Tag Recommendation based on Ranked Categorical Nearest Neighbors and Weighted Tag Features vol.5, pp.6, 2017, https://doi.org/10.25046/aj0506166