Skip to main content
SpringerLink
Log in

Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In this paper, a new self-organizing artificial neural network called growing hierarchical neural forest (GHNF) is proposed. The GHNF is a hierarchical model based on the growing neural forest, which is a tree-based model that learns a set of trees (forest) instead of a general graph so that the forest can grow in size. This way, the GHNF faces three important limitations regarding the self-organizing map: fixed size, fixed topology, and lack of hierarchical representation for input data. Hence, the GHNF is especially amenable to datasets containing clusters where each cluster has a hierarchical structure since each tree of the GHNF forest can adapt to one of the clusters. Experimental results show the goodness of our proposal in terms of self-organization and clustering capabilities. In particular, it has been applied to text mining of tweets as a typical exploratory data analysis application, where a hierarchical representation of concepts present in tweets has been obtained. Moreover, it has been applied to foreground detection in video sequences, outperforming several methods specialized in foreground detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://mmc36.informatik.uni-augsburg.de/VSSN06_OSAC/.

  2. http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html.

  3. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/.

  4. http://www.cs.cmu.edu/~yaser/.

  5. Reprinted from Computer Vision and Image Understanding, 133, Francisco Javier López-Rubio and Ezequiel López-Rubio, Features for stochastic approximation based foreground detection, 30–50, Copyright (2015), with permission from Elsevier.

References

  1. Aizawa A (2003) An information-theoretic perspective of tf-idf measures. Inf Process Manage 39(1):45–65

    Article  MATH  Google Scholar 

  2. Alahakoon D, Halgamuge SK, Srinivasan B (1998) A self-growing cluster development approach to data mining. In: 1998 IEEE international conference on systems, man, and cybernetics, 1998, vol 3, pp 2901–2906

  3. Ashok P, Kadhar G, Elayaraja E, Vadivel V (2013) Fuzzy based clustering method on yeast dataset with different fuzzification methods. In: 2013 Fourth international conference on computing, communications and networking technologies (ICCCNT), pp 1–6

  4. Astudillo CA, Oommen BJ (2014) Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations. Pattern Recogn 47(1):96–113

    Article  Google Scholar 

  5. Barbalho J, Duarte A, Neto D, Costa J, Netto M (2001) Hierarchical SOM applied to image compression. In: Marko K, Werbos P (eds) International joint conference on neural networks, 2001. Proceedings. IJCNN ’01, vol 1, pp 442–447

  6. Bertolini G, Ramat S (2007) A hierarchical SOM to identify and recognize objects in sequences of stereo images. Springer, Berlin, pp 977–981

    Google Scholar 

  7. Bhandarkar SM, Koh J, Suk M (1997) Multiscale image segmentation using a hierarchical self-organizing map. Neurocomputing 14(3):241–272

    Article  Google Scholar 

  8. Chen C, Yan F, Gao Y, Jin T, Zhou Z (2020) Improving reconstruction of sound speed profiles using a self-organizing map method with multi-source observations. Remote Sens Lett 11(6):572–580. https://doi.org/10.1080/2150704X.2020.1742940

    Article  Google Scholar 

  9. de Amorim RC, Hennig C (2015) Recovering the number of clusters in data sets with noise features using feature rescaling factors. Inf Sci 324(C):126–145

    Article  MathSciNet  Google Scholar 

  10. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407

    Article  Google Scholar 

  11. Fatima N, Rueda L (2020) iSOM-GSN: an integrative approach for transforming multi-omic data into gene similarity networks via self-organizing maps. Bioinformatics 36(15):4248–4254. https://doi.org/10.1093/bioinformatics/btaa500

  12. Fritzke B (1995) A growing neural gas network learns topologies. Adv Neural Inf Process Syst 7:625–632

    Google Scholar 

  13. Gemignani G, Rozza A (2016) A robust approach for the background subtraction based on multi-layered self-organizing maps. IEEE Trans Image Process 25(11):5239–5251

    Article  MathSciNet  MATH  Google Scholar 

  14. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press. http://www.deeplearningbook.org

  15. Granados A, Koroutchev K, de Borja Rodriguez F (2015) Discovering data set nature through algorithmic clustering based on string compression. IEEE Trans Knowl Data Eng 27(3):699–711

    Article  Google Scholar 

  16. Henriques R, Lobo V, Bação F (2012) Spatial clustering using hierarchical SOM, chap. 12. In: Johnsson M (ed) Applications of self-organizing maps. InTech, Rijeka

    Google Scholar 

  17. KaewTraKulPong P, Bowden R (2002) An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino P, Jones GA, Paragios N, Regazzoni C (eds) Video-Based Surveillance Systems. Springer, New York, pp 135–144

    Chapter  Google Scholar 

  18. Kim J, Billard L (2011) A polythetic clustering process and cluster validity indexes for histogram-valued objects. Comput Stat Data Anal 55(7):2250–2262

    Article  MathSciNet  MATH  Google Scholar 

  19. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69

    Article  MathSciNet  MATH  Google Scholar 

  20. Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65

    Article  Google Scholar 

  21. Koikkalainen P, Oja E (1990) Self-organizing hierarchical feature maps. In: 1990 IJCNN international joint conference on neural networks, vol 2, pp 279–284

  22. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  23. Kruskal JB (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc 7:48–50

    Article  MathSciNet  MATH  Google Scholar 

  24. Lampinen J, Oja E (1992) Clustering properties of hierarchical self-organizing maps. J Math Imaging Vis 2(2):261–272

    Article  MATH  Google Scholar 

  25. Liu Y, Takatsuka M (2009) Interactive hierarchical SOM for image retrieval visualization. Springer, Berlin, pp 845–854

    Google Scholar 

  26. López-Rubio FJ, López-Rubio E (2015) Features for stochastic approximation based foreground detection. Comput Vis Image Underst 133:30–50

    Article  Google Scholar 

  27. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. Trans Image Process 17(7):1168–1177

    Article  MathSciNet  Google Scholar 

  28. Maddalena L, Petrosino A (2010) A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput Appl 19(2):179–186

    Article  Google Scholar 

  29. Martinetz T, Schulten K (1991) A “neural-gas” network learns topologies. Artif Neural Netw I:397–402

    Google Scholar 

  30. Miikkulainen R (1990) Script recognition with hierarchical feature maps. Connect Sci 2:83–101

    Article  Google Scholar 

  31. Pakkanen J, Iivarinen J, Oja E (2004) The evolving tree—a novel self-organizing network for data analysis. Neural Process Lett 20(3):199–211

    Article  Google Scholar 

  32. Palomo EJ, López-Rubio E (2016) Learning topologies with the growing neural forest. Int J Neural Syst 26(4):1650019

    Article  Google Scholar 

  33. Palomo EJ, López-Rubio E (2017) The growing hierarchical neural gas self-organizing neural network. IEEE Trans Neural Netw Learn Syst 28(9):2000–2009

    MathSciNet  Google Scholar 

  34. Rahmel J (1996) SplitNet: learning of tree structured Kohonen chains. IEEE Int Conf Neural Netw 2:1221–1226

    Google Scholar 

  35. Rauber A, Merkl D, Dittenbach M (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans Neural Netw 13(6):1331–1341

    Article  MATH  Google Scholar 

  36. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  MATH  Google Scholar 

  37. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manage 24(5):513–523

    Article  Google Scholar 

  38. Samsonova EV, Kok JN, Ijzerman AP (2006) TreeSOM: cluster analysis in the self-organizing map. Neural Netw 19(6–7):935–949

    Article  MATH  Google Scholar 

  39. Sheikh Y, Shah M (2005) Bayesian modeling of dynamic scenes for object detection. IEEE Trans Pattern Anal Mach Intell 27(11):1778–1792

    Article  Google Scholar 

  40. Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on computer vision and pattern recognition, 1999, vol 2, pp 246–252

  41. Wren C, Azarbayejani A, Darrell T, Pentland A (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785

    Article  Google Scholar 

  42. Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Computer society conference on computer vision and pattern recognition. arXiv:1707.01083

  43. Zhu G, Wu X, Ge J, Liu F, Zhao W, Wu C (2020) Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a self-organizing map (SOM). J Clean Prod 257:120664

    Article  Google Scholar 

  44. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. Proc Int Conf Pattern Recognit 2:28–31

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the Ministry of Science, Innovation and Universities of Spain [Grant No. RTI2018-094645-B-I00], Project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under Project UMA18-FEDERJA-084, Project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. Finally, they are grateful to Mr Haritz Puerto-San-Román for providing the tweets dataset.

Author information

Authors and Affiliations

  1. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur 35, 29071, Málaga, Spain

    Esteban J. Palomo, Ezequiel López-Rubio, Francisco Ortega-Zamorano & Rafaela Benítez-Rochel

  2. Biomedic Research Institute of Málaga (IBIMA), C/ Doctor Miguel Díaz Recio, 28, 29010, Málaga, Spain

    Esteban J. Palomo, Ezequiel López-Rubio, Francisco Ortega-Zamorano & Rafaela Benítez-Rochel

Authors
  1. Esteban J. Palomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ezequiel López-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Ortega-Zamorano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafaela Benítez-Rochel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Esteban J. Palomo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palomo, E.J., López-Rubio, E., Ortega-Zamorano, F. et al. Exploratory Data Analysis and Foreground Detection with the Growing Hierarchical Neural Forest. Neural Process Lett 52, 2537–2563 (2020). https://doi.org/10.1007/s11063-020-10360-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10360-2

Keywords

Search

Navigation