Skip to main content
SpringerLink
Log in

A black hole novelty detector for video analysis

  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

A black hole is a region of space that has so much mass concentrated in it that there is no way training data for a nearby object to escape its gravitational pull. In this paper we are inspired by this phenomenon to create a new form of novelty detector. We consider the of a given class as a black hole. For multi-class data we are dealing with multiple black holes. A test point is pulled by the centroids of different black holes as well as its K nearest neighbours. The gravitational pull is modelled as an iterative process, where the forces acting on a point are constantly changing with time as the test point moves in multi-dimensional space corresponding to these forces. Once the algorithm has converged, a thresholding scheme is applied to determine whether the test point has been pulled within the boundary of the black hole or not. Any points that lie outside all known black holes are deemed to be novel. We compare this novelty detector with other well-known models of novelty detection on a video analysis application and show very promising results.

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

Similar content being viewed by others

References

  1. Augusteijn MF, Folkert BA (1999) Neural network classification and novelty detection. International Journal of Remote Sensing

  2. Bezdek J, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geo Science 10(2):191–203

    Google Scholar 

  3. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press

  4. Bishop C (1994) Novelty detection and neural network validation. In: Proc IEE Conf. on vision and image signal processing, 217–222

  5. Byungho H, Sungzoon C (1999) Characteristics of auto-associative MLP as a novelty detector. In: Proc IEEE IJCNN Conf. 5:3086–3091

    Google Scholar 

  6. Davies DL, Bouldin DW (1979) A cluster separation measure. In: IEEE Trans on pattern analysis and machine intelligence 1(2)

  7. Diaz I, Hollmen J (2002) Residual generation and visualization for understanding novel process conditions. In: Proc IEEE IJCNN Conference, 2070–2075

  8. Drimbarean A, Whelan PF (2001) Experiments in colour texture analysis. In: Pattern recognition letters 22:1161–1167

    Google Scholar 

  9. Duda RO, Hart RE, Stork DG (2001) Pattern classification, 2nd edn. John Wiley

  10. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernetics 3:32–57

    Google Scholar 

  11. Fleck MM, Forsyth DA, Bregler C (1996) Finding naked people. In: ECCV 2:592–602

    Google Scholar 

  12. Ford, Roberts A (1998) Colour space conversions. In: Tech Report University of Westminster

  13. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. In: IEEE Trans on systems, man and cybernetics 3(6):610–621

    Google Scholar 

  14. Huang J, Kumar S, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using colour correlogram. In: Proc of IEEE Conf on comput. Vision and pattern recognition, 762–768

  15. Japkowicz N, Myers C, Gluck M (1995) A novelty detection approach to classification. In: Proc of 14th IJCAI Conf, Montreal, 518–523

  16. Ko H, Jacyna G (2000) Dynamical behavior of autoassociative memory performing novelty filtering. IEEE Trans on neural networks 11(5):1152–1161

    Google Scholar 

  17. Kohonen T (1988) Self-organisation and associative memory. Springer-Verlag, Berlin

    Google Scholar 

  18. Kohonen T (2001) Self-organising maps. Springer, Berlin

    Google Scholar 

  19. Kundu S (1999) Gravitational clustering: a new approach based on the spatial distribution of the points. In: Pattern recognition 32:1149–1160

    Google Scholar 

  20. Laws KL (1980) Texture image segmentation. USCIPI Rep 940, University of Southern California

  21. LeCun Y et al (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, Morgan Kaufman, 2:396–404

  22. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. In: IEEE Transactions on Communications 28(1):84–95

    Google Scholar 

  23. Lloyd SP (1982) Least squares quantization in PCM. In: IEEE Trans information theory 28:129–137

    Google Scholar 

  24. Lou Z, Liu K, Yang JY, Suen CY (1999) Rejection criteria and pairwise discrimination of handwritten numerals based on structural features. In: Pattern Analysis and applications 2(3):228–238

    Google Scholar 

  25. Manevitz LM, Yousef M (2000) Learning from positive data for document classification using neural networks. In: Proc 2nd workshop on knowledge discovery and learning, Jerusalem

  26. Markou M, Singh M, Singh S (2001) Neural network analysis of Minerva scene analysis benchmark. In: Proc 11th international conf. on image analysis and processing, Italy

  27. Markou M, Singh M, Singh S (2002) Colour texture analysis of natural scene using neural networks. In: Proc international IEEE/INNS joint conf. on neural networks, Hawaii

  28. Markou M, Singh S (2003a) Novelty detection: a review, part I: statisticalapproaches. Signal processing 83:2481–2497

    Google Scholar 

  29. Markou M, Singh S (2003b) Novelty detection: a review, part II: neural network based approaches. Signal processing 83:2499–2521

    Google Scholar 

  30. Markou M (2005) Models of novelty detection based on machine learning. PhD Thesis, Department of Comput. science, University of Exeter, Exeter, UK

  31. Markou M, Singh S (2004) Feature selection based on a Black Hole model of data reorganization. In: 17th International conf. on pattern recognition (ICPR’04), Cambridge, pp 565–568

  32. Mindru F, Moons T, VanGool LJ (1999) Recognizing color patterns irrespective of viewpoint and illumination. In: Proc CVPR’99, 368–373

  33. Nabney IT (2002) Netlab: Algorithms for pattern recognition. Springer-Verlag, Berlin

    Google Scholar 

  34. Petsche T, Marcantonio A, Darken C, Hanson SJ, Kuhn GM, Santoso I (1996) A neural network autoassociator for induction motor failure prediction. Advances in NIPS 8:924–930

    Google Scholar 

  35. Plataniotis KN, Venetsanopoulos AN (2000) Colour image processing and applications. Springer-Verlag

  36. Ryan J, Lin MJ, Miikkulainen R (1998) Intrusion detection with neural networks. In: Jordan M et al (eds) Advances in Neural Information Processing Systems 10. MIT Press, pp 943–949

  37. Singh S, Markou M (2000) Learning and adaptation in scene analysis tasks using neural networks. In: Proc 6th International conf. on control, automation, robotics and vision, Singapore

  38. Singh S, Markou M, Haddon JF (2000a) Natural object classification using artificial neural networks. In: Proc IEEE International joint conf. on neural networks, Como, Italy, 3:139–144

  39. Singh S, Markou M, Haddon JF (2000b) Detection of new image objects in video sequences using neural networks. In: SPIE Conf. on applications of artificial neural networks in image processing, pp 204–213

  40. Singh S, Singh M, Markou M (2003) A comparison of feature extraction methods for scene analyzis. Workshop on artificial neural networks in pattern recognition, Italy, pp 108–113

  41. Singh S, Markou M (2004) An approach to novelty detection applied to the classification of image regions. IEEE Trans Knowl Data Eng 16(4):396–406

    Google Scholar 

  42. Song SO, Shin D, Yoon ES (2001) Analysis of novelty detection properties of Auto-Associators. In: Proc COMADEM, 577–584

  43. Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. (PWS Press)

  44. Surace C, Worden K (1998) A novelty detection method to diagnose damage in structures: an application to an off-shore platform. In: Proc 8th conf. of off-shore and polar eng 4:64–70

  45. Surace C, Worden K, Tomlinson G (1997) A novelty detection approach to diagnose damage in a cracked beam. In: Proc of SPIE 3089:947–953

    Google Scholar 

  46. Terrillon JC, Pilpre A, Niwa Y, Yamamoto K (2002) Analysis of a large set of colour spaces for skin pixel detection in colour images. In: 6th International conf. on quality control by artificial vision

  47. Terrillon JC, Pilpre A, Niwa Y, Yamamoto K (2003) Analysis of a large set of colour spaces for skin pixel detection in colour images. In: 6th International conf. on quality control by artificial vision

  48. Thompson BB, Marks RJ II, Choi JJ, El-Sharkawi MA, Huang M, Bunje C (2002) Implicit learning in auto-encoder novelty assessment. In: Proc Int’l Joint conf. on neural networks, 2878–2883

  49. Umbaugh SE (1998) Computer vision and image processing: a practical approach using CVIPtools

    Google Scholar 

  50. Unser M (1986) Local linear transforms for texture measurements. Signal Processing 11:61–79

    Google Scholar 

  51. Vesanto J, Alhoniemi E (2000) Clustering of the self-organising map. IEEE Trans on networks 11(3):586–600

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research School of Informatics, Loughborough University, Loughborough, LE11 3TU, UK

    Sameer Singh & Markos Markou

Authors
  1. Sameer Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markos Markou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sameer Singh.

Appendix

Appendix

Table 8 Appendix A. ‘Bin’ composition and Z values for the different novelty detection models
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Singh, S., Markou, M. A black hole novelty detector for video analysis. Pattern Anal Applic 8, 102–114 (2005). https://doi.org/10.1007/s10044-005-0248-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-005-0248-3

Keywords

Search

Navigation