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Image Annotation Algorithm Based on Semantic Similarity and Multi-features

  • Jingxiu Ni
  • Dongxing Wang
  • Guoying Zhang
  • Yanchao Sun
  • Xinkai Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

The paper proposed an image annotation algorithm based on semantic similarity and multi-feature fusion. The annotation algorithm draws lessons from the method of semantic extraction in natural language processing, and establishes the corresponding semantic trees for some common scenes. The scene semantic tree is constructed based on the visual features of the specific scene in the image set. Firstly, the visual features of scene images are extracted, and then the visual features are clustered by fuzzy clustering. According to the clustering results, the images are grouped, clustered at different nodes according to visual features, and the images are further grouped. After the scene semantic tree is constructed, the algorithm will extract the visual features of the image to be annotated. Furthermore, the image moves from the item node to a leaf node in the scene semantic tree according to its visual features, and the semantic keywords which appear in the route constitute the tags of the image.

Keywords

Semantic tree Image annotation Multi-feature fusion Semantic similarity Fuzzy clustering 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jingxiu Ni
    • 1
    • 2
  • Dongxing Wang
    • 2
  • Guoying Zhang
    • 2
  • Yanchao Sun
    • 2
  • Xinkai Xu
    • 1
    • 2
  1. 1.Beijing Union UniversityBeijingChina
  2. 2.School of Mechanical, Electronic and Information EngineeringChina University of Mining and TechnologyBeijingChina

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