research-article

An Approach to Novelty Detection Applied to the Classification of Image Regions

Authors:

Markos MarkouAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 16, Issue 4

Pages 396 - 407

https://doi.org/10.1109/TKDE.2004.1269665

Published: 01 April 2004 Publication History

Abstract

Abstract--In this paper, we present a new framework for novelty detection. The framework evaluates neural networks as adaptive classifiers that are capable of novelty detection and retraining on the basis of newly discovered information. We apply our newly developed model to the application area of object recognition in video. This paper details the tools and methods needed for novelty detection such that data from unknown classes can be reliably rejected without any a priori knowledge of its characteristics. The rejected data is postprocessed to determine which samples can be manually labeled of a new type and used for retraining. In this paper, we compare the proposed framework with other novelty detection methods and discuss the results of adaptive retraining of neural network to recognize further unseen data containing the newly added objects.

References

[1]

S. Albrecht J. Busch M. Kloppenburg F. Metze and P. Tavan, “Generalised Radial Basis Function Networks for Classification and Novelty Detection: Self-Organisation of Optimal Bayesian Decision,” Neural Networks, vol. 13, pp. 1075-1093, 2000.

Digital Library

[2]

J. Bezdek R. Ehrlich and W. Full, “FCM: The Fuzzy c-Means Clustering Algorithm,” Computers and Geosciences, vol. 10, no. 2, pp. 191-203, 1984.

[3]

C. Bishop, “Novelty Detection and Neural Network Validation,” Proc. IEE Conf. Vision and Image Signal Processing, pp. 217-222, 1994.

[4]

T. Brotherton T. Johnson and G. Chadderdon, “Classification and Novelty Detection Using Linear Models and a Class Dependent- Elliptical Basis Function Neural Network,” Proc. Int'l Joint Conf. Neural Networks, May 1998.

[5]

C. Campbell and K.P. Bennett, “A Linear Programming Approach to Novelty Detection,” Advances in Neural Information Processing Systems, vol. 14, 2001.

[6]

C.K. Chow, “On Optimum Rejection Error and Reject Tradeoff,” IEEE Trans. Information Theory, vol. 16, no. 1, pp. 41-46, 1970.

Digital Library

[7]

P. Crook and G. Hayes, “A Robot Implementation of a Biologically Inspired Method for Novelty Detection,” Proc. Towards Intelligent Mobile Robots Conf., 2001.

[8]

D. Dasgupta and F.A. Gonzalez, “An Immunogenetic Approach to Intrusion Detection,” Dept. of Computer Science, Univ. of Memphis, Report No. CS-01-001, May 2001.

[9]

M.J. Desforges P.J. Jacob and J.E. Cooper, “Application of Probability Density Estimation to the Detection of Abnormal Conditions in Engineering,” Proc. Inst. of Mechanical Eng., vol. 212, pp. 687-703, 1998.

[10]

R.O. Duda R.E. Hart and D.G. Stork, Pattern Classification, second ed. Wiley, 2001.

Digital Library

[11]

S.E. Fahlman and C. Lebiere, “The Cascade-Correlation Learning Architecture,” Advances in Neural Information Processing Systems, D.S. Touretzky, ed., vol. 2, pp. 524-532, 1990.

Digital Library

[12]

P. Foggia C. Sansone F. Tortorella and M. Vento, “Multiclassification: Reject Criteria for the Bayesian Combiner,” Pattern Recognition, vol. 32, pp. 1435-1447, 1999.

[13]

G. Fumera F. Roli and G. Giacinto, “Reject Option with Multiple Thresholds,” Pattern Recognition, vol. 33, pp. 2099-2101, 2000.

[14]

L.K. Hansen C. Liisberg and P. Salamon, “The Error-Reject Tradeoff,” Open Systems and Information Dynamics, vol. 4, pp. 159-184, 1997.

Digital Library

[15]

T. Harris, “Neural Network in Machine Health Monitoring,” Professional Eng., 1993.

[16]

J. Huang S. Kumar M. Mitra W.J. Zhu and R. Zahib, “Image Indexing Using Colour Correlogram,” Proc. IEEE Conf. Computer Vision and Recognition (CVPR '97), pp. 762-768, 1997.

Digital Library

[17]

N. Japkowicz C. Myers and M. Gluck, “A Novelty Detection Approach to Classification,” Proc. 14th Int'l Joint Conf. Artificial Intelligence, pp. 518-523, 1995.

Digital Library

[18]

T. Kwok and D. Yeung, “Objective Functions for Training New Hidden Units in Constructive Neural Networks,” IEEE Trans. Neural Networks, vol. 8 no. 5, pp. 1131-1148, 1999.

Digital Library

[19]

J. Lamirel M. Crehange and J. Dulcoy, “NOMAD: A Documentary Database Interrogation System Using Multiple Neural Topographies and Novelty Detection,” Advances in Knowledge Organisation, vol. 4, pp. 334-341, 1994.

[20]

M.A. Lewis and L.S. Simo, “Certain Principles of Biomorphic Robots,” Autonomous Robots, 2001.

Digital Library

[21]

Y. Li M.J. Pont and N.B. Jones, “Improving the Performance of the Radial Basis Function Classifiers in Condition Monitoring and Fault Diagnosis Applications where ‘Unknown’ Faults May Occur,” Pattern Recognition Letters, vol. 23, pp. 569-577, 2002.

Digital Library

[22]

S. Marsland U. Nehmzow and J. Shapiro, “A Model of Habituation Applied to Mobile Robots,” Proc. Towards Intelligent Mobile Robots Conf., 1999.

[23]

S. Marsland U. Nehmzow and J. Shapiro, “A Real-Time Novelty Detector for a Mobile Robot,” Proc. European Advanced Robotics Systems Conf., 2000a.

[24]

S. Marsland U. Nehmzow and J. Shapiro, “Novelty Detection for Robot Neotaxis,” Proc. Second Int'l ICSC Symp. Neural Computation, pp. 554-559, 2000b.

[25]

S. Marsland U. Nehmzow and J. Shapiro, “Detecting Novel Features of an Environment Using Habituation,” Proc. Simulation of Adaptive Behaviour, 2000c.

[26]

S. Marsland U. Nehmzow and J. Shapiro, “Novelty Detection in Large Environments,” Proc. Towards Intelligent Mobile Robots Conf., 2001.

[27]

F. Mindru T. Moons and L. VanGool, “Colour-Based Moment Invariants for Viewpoint and Illumination Independent Recognition of Colour Patterns,” Proc. Int'l Conf. Pattern Recognition (ICPR '98), pp. 113-124, 1998.

[28]

A.F. Murray, “Novelty Detection Using Products of Simple Experts-A Potential Architecture for Embedded Systems,” Neural Networks, vol. 14, pp. 1257-1264, 2001.

Digital Library

[29]

A. Nairac T. Corbett-Clark R. Ripley N. Townsend and L. Tarassenko, “Choosing an Appropriate Model for Novelty Detection,” Proc. Fifth Int'l Conf. Artificial Neural Networks, pp. 227-232, 1997.

[30]

A. Nairac N. Townsend R. Carr S. King P. Cowley and L. Tarassenko, “A System for the Analysis of Jet Engine Vibration Data,” Integrated Computer Aided Eng., vol. 6, pp. 53-65, 1999.

Digital Library

[31]

J.A. Parikh, “A Comparative Study of Cloud Classification Strategies,” Remote Sensing of the Environment, vol. 6, pp. 67-81, 1977.

[32]

L. Parra G. Deco and S. Miesbach, “Statistical Independence and Novelty Detection with Information Preserving Non-Linear Maps,” Neural Computation, vol. 8, pp. 260-269, 1995.

Digital Library

[33]

N.J. Pizzi R.A. Vivanco and R.L. Somorjai, “EvIdent: A Functional Magnetic Resonance Image Analysis System,” Artificial Intelligence in Medicine, vol. 21, pp. 263-269, 2001.

Digital Library

[34]

K.N. Plataniotis and A.N. Venetsanopoulos, Color Image Processing and Applications. Springer-Verlag, 2000.

Digital Library

[35]

S.J. Roberts and W. Penny, “Novelty, Confidence and Errors in Connectionist Systems,” Proc. IEE Colloquium Intelligent Sensors and Fault Detection, no. 1996/261, 1996.

[36]

S.J. Roberts, “Novelty Detection Using Extreme Value Statistics,” IEE Proc. Vision, Image and Signal Processing, vol. 146, issue 3, pp. 124-129, 1999.

[37]

R. Ruotolo C. Surace and K. Worden, “Application of Two Damage Detection Techniques to an Off-Shore Platform,” Proc. SPIE, vol. 3727, pp. 882-888, 1999.

[38]

M. Saptharishi K. Bhat C. Diehl C. Oliver M. Savvides A. Soto J. Dolan and P. Khosla, “Recent Advances in Distributed Collaborative Surveillance,” SPIE Proc. Unattended Ground Sensor Technologies and Applications (AeroSense 2000), vol. 4040, pp. 199-208, 2000.

[39]

R. Saunders and J.S. Gero, “The Importance of Being Emergent,” Proc. AI Design, 2000.

[40]

B. Schölkopf R. Williamson A. Smola J.S. Taylor and J. Platt, “Support Vector Method for Novelty Detection,” Neural Information Processing Systems, S.A. Solla et al., eds., pp. 582-588, 2000.

[41]

M. Singh and S. Singh, “Minerva Scene Analysis Benchmark,” Proc. Seventh Australian and New Zealand Intelligent Information Systems Conf., pp. 231-235, 2001.

[42]

S. Singh M. Markou and J.F. Haddon, “Detection of New Image Objects in Video Sequences Using Neural Networks,” Proc. SPIE Electronic Imaging '2000, pp. 204-213, 2000.

[43]

H. Sohn K. Worden and C.R. Farrar, “Novelty Detection Using Auto-Associative Neural Network,” Proc. Symp. Identification of Mechanical Systems: Int'l Mechanical Eng. Congress and Exposition, 2001.

[44]

R.J. Streifel R.J. Maks and M.A. El-Sharkawi, “Detection of Shorted-Turns in the Field of Turbine-Generator Rotors Using Novelty Detectors-Development and Field Tests,” IEEE Trans. Energy Conversation, vol. 11, no. 2, pp. 312-317, 1996.

[45]

C. Surace and K. Worden, “Some Aspects of Novelty Detection Methods,” Modern Practices in Stress and Vibration Analysis, pp. 89-94, 1997.

[46]

C. Surace and K. Worden, “A Novelty Detection Method to Diagnose Damage in Structures: An Application to An Offshore Platform,” Proc. Eighth Int'l Conf. Off-Shore and Polar Eng., vol. 4, pp. 64-70, 1998.

[47]

C. Surace K. Worden and G. Tomlinson, “A Novelty Detection Approach to Diagnose Damage in a Cracked Beam,” Proc. SPIE, vol. 3089, pp. 947-953, 1997.

[48]

D.M.J. Tax and R.P.W. Duin, “Support Vector Domain Description,” Pattern Recognition Letters, vol. 20, pp. 1191-1199, 1999.

Digital Library

[49]

L. Tarassenko, “Novelty Detection for the Identification of Masses in Mammograms,” Proc. Fourth Int'l Conf. Artificial Neural Networks, vol. 4, pp. 442-447, 1995.

[50]

L. Tarassenko A. Nairac N. Townsend and P. Cowley, “Novelty Detection in Jet Engines,” Proc. IEE Colloqium Condition Monitoring, Imagery, External Structures and Health, pp. 41-45, 1999.

[51]

O. Taylor J. Tait and J. MacIntyre, “Improved Classification for a Data Fusing Kohonen Self Organising Map Using a Dynamic Thresholding Techniques,” Proc. 16th Int'l Joint Conf. Artificial Intelligence, vol. 2, pp. 828-832, 1999.

Digital Library

[52]

G.C. Vasconcelos, “A Bootstrap-Like Rejection Mechanism for Multilayer Perceptron Networks,” II Simposio Brasileiro de Redes Neurais, pp. 167-172, 1995.

[53]

G.C. Vasconcelos M.C. Fairhurst and D.L. Bisset, “Recognizing Novelty in Classification Tasks,” Proc. NIPS Conf. Novelty Detection, Adaptive Systems Monitoring, 1994.

[54]

J. Vesanto and E. Alhoniemi, “Clustering of the Self-Organising Map,” IEEE Trans. Neural Networks, vol. 11, no. 3, pp. 586-600, 2000.

Digital Library

[55]

K. Worden, “Structural Fault Detection Using a Novelty Measure,” J. Sound and Vibration, vol. 201, issue 1, pp. 85-101, 1997.

Cited By

Hendrickx KPerini LVan der Plas DMeert WDavis J(2024)Machine learning with a reject option: a surveyMachine Language10.1007/s10994-024-06534-x113:5(3073-3110)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06534-x
Agrahari SSrivastava SSingh A(2024)Review on novelty detection in the non-stationary environmentKnowledge and Information Systems10.1007/s10115-023-02018-x66:3(1549-1574)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-02018-x
Nair B(2024)Predicting document novelty: an unsupervised learning approachKnowledge and Information Systems10.1007/s10115-023-01989-166:3(1709-1728)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-01989-1
Show More Cited By

Index Terms

An Approach to Novelty Detection Applied to the Classification of Image Regions
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Neural networks

Recommendations

A Neural Network-Based Novelty Detector for Image Sequence Analysis

This paper proposes a new model of "novelty detection” for image sequence analysis using neural networks. This model uses the concept of artificially generated negative data to form closed decision boundaries using a multilayer perceptron. The neural ...
Novelty detection: a review—part 2: neural network based approaches

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered ...
Hierarchical Novelty Detection
Advances in Intelligent Data Analysis XVI
Abstract
Hierarchical classification is commonly defined as multi-class classification where the classes are hierarchically nested. Many practical hierarchical classification problems also share features with multi-label classification (i.e., each data ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 16, Issue 4

April 2004

144 pages

ISSN:1041-4347

Issue’s Table of Contents

Copyright © Copyright © 2004 IEEE. All Rights Reserved.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 April 2004

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

26
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hendrickx KPerini LVan der Plas DMeert WDavis J(2024)Machine learning with a reject option: a surveyMachine Language10.1007/s10994-024-06534-x113:5(3073-3110)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06534-x
Agrahari SSrivastava SSingh A(2024)Review on novelty detection in the non-stationary environmentKnowledge and Information Systems10.1007/s10115-023-02018-x66:3(1549-1574)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-02018-x
Nair B(2024)Predicting document novelty: an unsupervised learning approachKnowledge and Information Systems10.1007/s10115-023-01989-166:3(1709-1728)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-01989-1
Kerner HWellington DWagstaff KBell JKwan CAmor H(2019)Novelty detection for multispectral images with application to planetary explorationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33019484(9484-9491)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33019484
Zamzami NBouguila N(2019)Model selection and application to high-dimensional count data clusteringApplied Intelligence10.1007/s10489-018-1333-949:4(1467-1488)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10489-018-1333-9
Aguayo LBarreto G(2018)Novelty Detection in Time Series Using Self-Organizing Neural NetworksNeural Processing Letters10.1007/s11063-017-9679-247:2(717-744)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11063-017-9679-2
Faria EGonçalves ICarvalho AGama J(2016)Novelty detection in data streamsArtificial Intelligence Review10.1007/s10462-015-9444-845:2(235-269)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1007/s10462-015-9444-8
Nguyen TNguyen ANguyen TVu LNguyen QHai LThang HPhuong LDe Raedt LDeville YBui MLinh TOanh NSang DNgoc N(2015)Unsupervised Anomaly Detection in Online GameProceedings of the 6th International Symposium on Information and Communication Technology10.1145/2833258.2833305(4-10)Online publication date: 3-Dec-2015
https://dl.acm.org/doi/10.1145/2833258.2833305
Lee EFung ITam VArashpour M(2014)A fully autonomous kernel-based online learning neural network model and its application to building cooling load predictionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-013-1181-918:10(1999-2014)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s00500-013-1181-9
Zhang JYe LXiang YZhou W(2013)Robust image retrieval with hidden classesComputer Vision and Image Understanding10.1016/j.cviu.2013.02.008117:6(670-679)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1016/j.cviu.2013.02.008
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents