Computer Science ›› 2019, Vol. 46 ›› Issue (7): 246-251.doi: 10.11896/j.issn.1002-137X.2019.07.037

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Image Annotation Based on Topic Fusion and Frequent Patterns Mining

ZHANG Lei,CAI Ming   

  1. (College of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)
  • Received:2018-05-30 Online:2019-07-15 Published:2019-07-15

Abstract: In order to reduce the “semantic gap”,based on the LDA topic model,an image annotation approach which uses topics fusion and association rule mining was proposed.First,to solve the problem of low correlation between visualand text information,the vector machine-based multi-category classification is introduced to obtain the category information of the image.Then,the text topic distribution of the image class is calculated by the semantic topic distribution and classification information of the text modality.The unknown image weights the text topic distribution of its class and its visual topic distribution,and calculates the initial label set using this probability model.Finally,based on the probability of initial label words,the association rules mining and inter-word correlation are used to mine the text relevance to obtain precise semantic annotation.The comparative experiments were carried out on the Corel5K image dataset.The experimental results show the effectiveness of the proposed method.

Key words: Image annotation, LDA topic model, Weighted topic fusion, Frequent patterns mining, Correlation of keyword.

CLC Number: 

  • TP391
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