PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Automated approach to classification of mine-like objects using multiple-aspect sonar images

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.
Rocznik
Strony
133--148
Opis fizyczny
Bibliogr. 49 poz., rys.
Twórcy
autor
  • Faculty of Computer Science Dalhousie University, Canada
autor
  • Faculty of Computer Science Dalhousie University, Canada
autor
  • School of Electrical Engineering & Computer Science University of Ottawa, Canada
autor
  • Faculty of Computer Science, Dalhousie University, Canada Institute of Computer Science, Polish Academy of Sciences, Poland
Bibliografia
  • [1] B. Zerr, B. Stage. Three-dimensional reconstruction of underwater objects from a sequence of sonar images, Proceedings of the IEEE International Conference on Image Processing, pp. 927–930, (1996).
  • [2] B. Zerr, B. Stage and A. Guerrero, Automatic Target Classification Using Multiple Sidescan Sonar Images of Different Orientations, SACLANT CEN Memorandum SM-309 (1997).
  • [3] B. Zerr, E. Bovio, B. Stage, Automatic mine classification approach based on AUV maneuverability and cots side scan sonar, Proceedings of Goats 001 Conference, La Spezia, Italy, (2001).
  • [4] M. Couillard, J. Fawcett, M. Davison and V. Myers, Optimizing time-limited multi-aspect classification, Proceedings of the Institute of Acoustics 29(6), 89-96 (2007).
  • [5] J. Fawcett, V. Myers, D. Hopkin, A. Crawford, M. Couillard, B. Zerr. Multiaspect classification of sidescan sonar images: Four different approaches to fusing single-aspect information, Oceanic Engineering, IEEE Journal of 35(4): 863 –876 (2010).
  • [6] S. Reed, Y. Petillot, J. Bell, Model-based approach to the detection and classification of mines in side scan sonar, Applied Optics 43(2): 237– 246. (2004).
  • [7] S. Reed, Y. Petillot, J. Bell, Automated approach to classification of mine-like features in sidescan sonar using highlight and shadow information, IEE Proc. Radar, Sonar & Navigation 151 (No.1), 48-56, (2004).
  • [8] V. Myers, D. P. Williams, A POMDP for multiview target classification with an autonomous underwater vehicle, OCEANS, pp. 1-5, (2010).
  • [9] V. Myers, D. P. Williams, Adaptive Multiview Target Classification in Synthetic Aperture Sonar Images Using a Partially Observable Markov Decision Process, Oceanic Engineering, IEEE Journal of, On page(s): 45 - 55, Volume: 37 Issue: 1, Jan. (2012)
  • [10] D. Williams, V. Myers, and M. Silvious, ”Mine Classification with Imbalanced Data,” IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 3, pp. 528-532, July 2009.
  • [11] G. Dobeck, “Fusing sonar images for mine detection and classification,” Proc. SPIE—Int. Soc. Opt. Eng., vol. 3710, 1999, DOI: 10.1117/12.357082.
  • [12] J. Tucker, N. Klausner, and M. Azimi-Sadjadi, “Target detection in M-disparate sonar platformsusing multichannel hypothesis testing,” in Proc. OCEANS Conf., Quebec City, QC, Canada, 2008, DOI: 10.1109/ OCEANS.2008.5151818.
  • [13] M. Azimi-Sadjadi, A. Jamshidi, and G. Dobeck, “Adaptive underwater target classification with multi-aspect decision feedback,” Proc. SPIE—Int. Soc. Opt. Eng., vol. 4394, 2001, DOI: 10.1117/12.445444.
  • [14] X. Xu and E. Frank. Logistic regression and boosting for labeled bags of instances. In Lecture Notes in Computer Science, volume 3056, pages 272–281, April 2004.
  • [15] Hall, D. L. and Steinberg, A.. Dirty Secrets in Multisensor Data Fusion, http://www.dtic.mil. (2001).
  • [16] Blockeel, H., Page, D., Srinivasan, A.: Multiinstance tree learning. In: ICML (2005).
  • [17] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, (1993).
  • [18] Hosmer, David W.; Lemeshow, Stanley. Applied Logistic Regression (2nd ed.). Wiley. (2000).
  • [19] Freund,Y., Schapire,R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156 (1996)
  • [20] Kubat, M., & Matwin, S.: Addressing the curse of imbalanced training sets: One-sided selection. In: Proceddings of the Fourteenth International Conference on Machine Learning, 179-186 (1997)
  • [21] Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the multiple instance problem with the axisparallel rectangles. In: Artificial Intelligence, 89(1-2), 31–71 (1997)
  • [22] Maron, O., Lozano-Pz T.: A framework for multiple instance learning. In: Proc. of the 1997 Conf. on Advances in Neural Information Processing Systems 10, p.570-576 (1998)
  • [23] Fan, W., Stolfo,S.J., Zhang,J., Chan,P.K.: Ada-Cost: Misclassification Cost-Sensitive Boosting. In: Proc. Int’l Conf. Machine Learning, pp. 97-105 (1999)
  • [24] Schapire,R.E., Singer,Y.: Improved boosting algorithms using confidence-rated predictions. In: Machine Learning, 37 (3) 297–336 (1999)
  • [25] Ting, K.M.: A Comparative Study of Cost-Sensitive Boosting Algorithms. In: Proc. Int’l Conf. Machine Learning, pp. 983-990 (2000)
  • [26] Wang, J., Zucker, J.D.: Solving the multipleinstance problem: A lazy learning approach. In: ICML (2000)
  • [27] Japkowicz, N.: Learning from Imbalanced Data Sets: A Comparison of Various Strategies. In: Proc. Am. Assoc. for Artificial Intelligence(AAAI) Workshop Learning from Imbalanced Data Sets, pp. 10-15. (Technical Report WS-00-05) (2000)
  • [28] Zhang, Q., Goldman, S. A.: EM-DD: An improved multiple instance learning technique. In: Neural Information Processing Systems 14 (2001)
  • [29] Elkan, C.: The Foundations of Cost-Sensitive Learning. In: Proc. Int’l Joint Conf. Artificial Intelligence, pp. 973-978 (2001)
  • [30] Ting, K.M.: An Instance-Weighting Method to Induce Cost-Sensitive Trees. In: IEEE Trans. Knowledge and Data Eng., vol. 14, no. 3, pp. 659-665 (2002)
  • [31] Chawla,N. V., Bowyer,K. W., Hall,L. O., Kegelmeyer, W. P.: SMOTE: Synthetic Minority Over-sampling Technique. In: Journal of Artificial Intelligence Research, 16: 321-357 (2002)
  • [32] Zhang, M.L., Goldman, S.: Em-dd: An improved multi-instance learning technique. In: NIPS (2002)
  • [33] Andrews, S., Tsochandaridis, I., Hofman, T.: Support vector machines for multiple instance learning. In: Adv. Neural. Inf. Process. Syst. 15, 561–568 (2003)
  • [34] Batista, G.E.A.P.A., Prati,R.C., Monard,M.C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. In: ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 20-29 (2004)
  • [35] Blockeel, H., Page, D., Srinivasan, A.: Multiinstance tree learning. In: ICML (2005)
  • [36] Sun,Y., Kamel,M.S., Wong, A.K.C., Wang, Y.: Cost-Sensitive Boosting for Classification of Imbalanced Data. In: Pattern Recognition, vol. 40, no. 12, pp. 3358-3378 (2007)
  • [37] Foulds, J., Frank, E.: Revisiting multiple-instance learning via embedded instance selection. In: W. Wobcke & M. Zhang(Eds), 21st Australasian Joint Conference on Artificial Intelligence Auckland,New Zealand, (pp. 300-310) (2008)
  • [38] Leistner, C., Saffari, A., and Bischof, H.: MIForests: Multiple Instance Learning with Randomized Trees. In: Proc. ECCV (2010)
  • [39] Bjerring, L., Frank, E.: Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data. In: D. Wang & M. Reynolds (Eds.), AI 2011, LNAI 7106 (pp. 41-50) (2011)
  • [40] Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press (2011)
  • [41] Shawe-Taylor, J. and Cristianini, N.: Further results on the margin distribution. In: Proceedings of the 12th Conference on Computational Learning Theory, 278-285 (1999)
  • [42] Morik, K., Brockhausen, P., Joachims, T.: Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. In: ICML: 268-277 (1999)
  • [43] Veropoulos, K., Campbell, C., & Cristianini, N.: Controlling the sensitivity of support vector machines. In: Proceedings of the International Joint Conference on Artificial Intelligence, 55–60. (1999)
  • [44] Chih-Chung Chang and Chih-Jen Lin.: LIBSVM : a library for support vector machines. In: ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, (2011)
  • [45] Chin-Wei Hsu, Chih-Chung Chang and Chih-Jen Lin.: A practical guide to support vector classification. In: Technical Report, National Taiwan University. (2010)
  • [46] Bergstra, James; Bengio, Yoshua. :Random Search for Hyper-Parameter Optimization. In: J. Machine Learning Research 13: 281-305. (2012)
  • [47] Wang, X., Shao, H., Japkowicz, N., Matwin, S., Liu, X., Bourque, A., Nguyen, B.: Using SVM with Adaptively Asymmetric Misclassification Costs for Mine-Like Objects Detection. In: ICMLA (2012)
  • [48] Wang, X., Matwin, S., Japkowicz, N., Liu, X.: Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets. In: Canadian Conference on AI (2013)
  • [49] Hosmer, David W. Lemeshow, Stanley: Applied Logistic Regression. Wiley. ISBN 0-471-35632-8.(2000)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-65e0ac73-d335-44c0-accf-156991cad750
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.