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Latent Dirichlet Allocation Based Image Retrieval

  • Jing Hao
  • Hongxi WeiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)

Abstract

In recent years, Bag-of-Visual-Word (BoVW) model has been widely used in computer vision. However, BoVW ignores not only spatial information but also semantic information between visual words. In this study, a latent Dirichlet allocation (LDA) based model has been proposed to obtain the semantic relations of visual words. Because the LDA-based topic model used alone usually degrade performance. Thus, a visual language model (VLM) is combined with LDA-based topic model linearly to represent each image. On our dataset, the proposed approach has been compared with state-of-the-art approaches (such as BoVW, LLC, SPM and VLM). Experimental results indicate that the proposed approach outperforms the original BoVW, LLC, SPM and VLM.

Keywords

Image retrieval Latent dirichlet allocation Visual language model Query likelihood model Smoothing 

Notes

Acknowledgements

The paper is supported by the National Natural Science Foundation of China under Grant 61463038.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer ScienceInner Mongolia UniversityHohhotChina

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