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Latent Dirichlet Allocation Based Image Retrieval

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Information Retrieval (CCIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10390))

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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.

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References

  1. Chen, X., Hu X., Shen, X.: Spatial weighting for bag-of-visual-words and its application in content-based image retrieval. In: Proceedings of PAKDD 2009, pp. 867–874. ACM Press, New York (2009)

    Google Scholar 

  2. Willamowski, J., Arregui, D., Csurka, G., et al.: Categorizing nine visual classes using local appearance descriptors. In: Proceedings of ICPR Workshop on Learning for Adaptable Visual Systems. IEEE Press, New York (2004)

    Google Scholar 

  3. Yuan, J., Wu, Y., Yang, M.: Discovery of collocation patterns: from visual words to visual phrases. In: Proceedings of CVPR 2007, pp. 1–8. IEEE Press, New York (2007)

    Google Scholar 

  4. Cao, Y., Wang, C., Li, Z., et al.: Spatial-bag-of-features. In: Proceedings of CVPR 2010, pp. 3352–3359. IEEE Press, New York (2010)

    Google Scholar 

  5. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR 2006, pp. 2169–2178. IEEE Press, New York (2006)

    Google Scholar 

  6. Wang, J., Yang, J., Yu, K., et al.: Locality-constrained linear coding for image classification. In: Proceedings of CVPR 2010, pp. 3360–3367. IEEE Press, New York (2010)

    Google Scholar 

  7. Harada, T., Ushiku, Y., Yamashita, Y., et al.: Discriminative spatial pyramid. In: Proceedings of CVPR 2011, pp. 1617–1624. IEEE Press, New York (2011)

    Google Scholar 

  8. Ren, Y., Bugeau, A., Benois-Pineau, J.: Bag-of-bags of words irregular graph pyramids vs spatial pyramid matching for image retrieval. In: Proceedings of IPTA 2014, pp. 1–6. IEEE Press, New York (2014)

    Google Scholar 

  9. Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3), 316–336 (2010)

    Article  Google Scholar 

  10. Li, F.F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. J. Comput. Vis. Image Underst. 106(1), 59–70 (2007)

    Article  Google Scholar 

  11. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of ICCV 1999, pp. 1150–1157. IEEE Press, New York (1999)

    Google Scholar 

  12. Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of SIGIR 2001, pp. 334–342. ACM Press, New York (2001)

    Google Scholar 

  13. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  14. Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of SIGIR 2006, pp. 178–185. ACM Press, New York (2006)

    Google Scholar 

  15. Wei, H., Gao, G., Su, X.: LDA-based word image representation for keyword spotting on historical mongolian documents. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9950, pp. 432–441. Springer, Cham (2016). doi:10.1007/978-3-319-46681-1_52

    Chapter  Google Scholar 

  16. Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. School of Computer Science, Inner Mongolia University, Hohhot, 010021, China

    Jing Hao & Hongxi Wei

Authors
  1. Jing Hao
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  2. Hongxi Wei
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Corresponding author

Correspondence to Hongxi Wei .

Editor information

Editors and Affiliations

  1. Renmin University, Beijing, China

    Jirong Wen

  2. Université de Montréal, Montreal, Canada

    Jianyun Nie

  3. East China University of Science and Technology, Shanghai, China

    Tong Ruan

  4. Tsinghua University, Beijing, China

    Yiqun Liu

  5. Wuhan University, Wuhan, Hubei, China

    Tieyun Qian

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Hao, J., Wei, H. (2017). Latent Dirichlet Allocation Based Image Retrieval. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-68699-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68698-1

  • Online ISBN: 978-3-319-68699-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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