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Soft Computing

, Volume 19, Issue 4, pp 829–838 | Cite as

Overlapped latent Dirichlet allocation for efficient image segmentation

  • Young-Seob JeongEmail author
  • Ho-Jin Choi
Focus

Abstract

Unsupervised methods for image segmentation have recently drawn significant attention because most images do not have labels or tags. A topic model is an unsupervised probabilistic method that captures the latent aspects of data, where each latent aspect or topic is associated with one homogeneous region. In this paper, we propose a new topic model for image segmentation task that incorporates spatial information into its structure based on the hypothesis that overlapped topic proportions convey spatial information. The model is efficient in time and memory, and we demonstrate this through comparison with other models using the MSRC image dataset.

Keywords

Probabilistic topic model  Image segmentation Spatial information 

Notes

Acknowledgments

This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD060048AD).

References

  1. Abdi H, Williams LJ (2010) Distinctive image features from scale-invariant keypoints. Wiley Interdiscip Rev: Comput Stat 2(4):433–459CrossRefGoogle Scholar
  2. Andrieu C, de Freitas N, Doucet A, Jordan MI (2003) An introduction to MCMC for machine learning. J Mach Learn 50(1):5–43CrossRefzbMATHGoogle Scholar
  3. Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd International Conference on machine learning. Pittsburgh, pp 113–120Google Scholar
  4. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  5. Burns TJ, Corso JJ (2009) Robust unsupervised segmentation of degraded document images with topic models. In: Proceedings of 2009 IEEE Conference on computer vision and pattern recognition. Miami, pp 1287–1294Google Scholar
  6. Cao L, Fei-Fei L (2007) Spatially coherent latent topic model for concurrent object segmentation and classification. In: Proceedings of 2007 IEEE International Conference on computer vision. Rio de Janeiro, pp 1–8Google Scholar
  7. Cao Y, Wang C, Li Z, Zhang L, Zhang L (2007) Spatial-bag-of-features. In: Proceedings of 2007 IEEE Conference on computer vision and pattern recognition. San Francisco, pp 3352–3359Google Scholar
  8. Chang J, Boyd-Graber J, Blei DM (2009) Connections between the lines: augmenting social networks with text. In: Proceedings of the 15th ACM SIGKDD International Conference on knowledge discovery and data mining. France, Paris, pp 169–178Google Scholar
  9. Chen X, Hu X, Shen X (2009) Spatial weighting for bag-of-visual-words and its application in content-based image retrieval. Lect Notes Computer Sci 5476:867–874CrossRefGoogle Scholar
  10. Doggaz N, Ferjani I (2011) Image segmentation using normalized cuts and efficient graph-based segmentation. In: Proceedings of the 16th International Conference on image analysis and processing. Ravenna, pp 229–240Google Scholar
  11. Du L, Buntine WL, Jin H (2010) Sequential latent Dirichlet allocation: discover underlying topic structures within a document. In: Proceedings of 2010 IEEE International Conference on data mining. Sydney, pp 148–157Google Scholar
  12. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Computer Vis 59(2):167–181CrossRefGoogle Scholar
  13. Griffiths TL, Steyvers M (2004) Finding scientific topics. In: Proceedings of the National Academy of Sciences of the United States of America, pp 5228–5235Google Scholar
  14. Hofmann T (1999) Probabilistic latent semantic analysis. In: Proceedings of Uncertainty in artificial intelligence. Stockholm, pp 289–296Google Scholar
  15. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle River, NJGoogle Scholar
  16. Jeong YS, Choi HJ (2012) Sequential entity group topic model for getting topic flows of entity groups within one document. Lect Notes Computer Sci 7301:366–378CrossRefGoogle Scholar
  17. Kasson JM, Plouffe W (1992) An analysis of selected computer interchange color spaces. ACM Trans Graphics 11:373–405CrossRefzbMATHGoogle Scholar
  18. Li LJ, Socher R, Fei-Fei L (2009) Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In: Proceedings of 2009 IEEE Conference on computer vision and pattern recognition. Miami, pp 1–8Google Scholar
  19. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–95CrossRefGoogle Scholar
  20. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Computer Vis 60(2):91–110CrossRefGoogle Scholar
  21. Mbanya E, Gerke S, Ndjiki-Nya P (2011) Spatial codebooks for image categorization. In: Proceedings of 1st ACM International Conference on multimedia retrieval. Trento, pp 1–7Google Scholar
  22. Newman D, Chemudugunta C, Smyth P (2006) Statistical entity-topic models. In: Proceedings of the 12th ACM SIGKDD International Conference on knowledge discovery and data mining. Philadelphia, pp 680–686Google Scholar
  23. Niu Z, Hua G, Gao X, Tian Q (2011) Spatial-disclda for visual recognition. In: Proceedings of 2011 IEEE Conference on computer vision and pattern recognition. Colorado Springs, USA, pp 1769–1776Google Scholar
  24. Park SM, Park J, Kim HB, Sim KB (2011) Specified object tracking problem in an environment of multiple moving objects. Int J Fuzzy Logic Intell Syst 11(2):118–123 Google Scholar
  25. Philbin J, Sivic J, Zisserman A (2011) Geometric latent Dirichlet allocation on a matching graph for large-scale image datasets. Int J Computer Vis 95(2):138–153CrossRefzbMATHMathSciNetGoogle Scholar
  26. Shechtman E, Irani M (2007) Matching local self-similarities across images and videos. In: Proceedings of 2007 IEEE Conference on computer vision and pattern recognition. San Francisco, pp 1800–1807Google Scholar
  27. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905CrossRefGoogle Scholar
  28. Wang X, Grimson E (2007) Spatial latent Dirichlet allocation. In: Proceedings of 21st Annual Conference on neural information processing systems. Vancouver, pp 1–8Google Scholar
  29. Winn J, Criminisi A, Minka T (2005) Object categorization by learned universal visual dictionary. In: Proceedings of the 2005 IEEE International Conference on computer vision. Beijing, pp 1800–1807Google Scholar
  30. Xie W, Xu D, Feng S, Tang Y (2010) Feature selection based codebooks construction for scene categorization. In: Proceedings of International Conference on signal processing. Beijing, pp 948–951Google Scholar
  31. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45CrossRefGoogle Scholar
  32. Zhao B, Fei-Fei L, Xing EP (2010) Image segmentation with topic random field. In: Proceedings of the 11th European Conference on computer vision: part V. Crete, pp 785–798Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceKAISTDaejeonSouth Korea

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