1. bookVolume 4 (2014): Edizione 2 (April 2014)
Dettagli della rivista
License
Formato
Rivista
eISSN
2449-6499
Prima pubblicazione
30 Dec 2014
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese
Accesso libero

Automated Approach To Classification Of Mine-Like Objects Using Multiple-Aspect Sonar Images

Pubblicato online: 01 Mar 2015
Volume & Edizione: Volume 4 (2014) - Edizione 2 (April 2014)
Pagine: 133 - 148
Dettagli della rivista
License
Formato
Rivista
eISSN
2449-6499
Prima pubblicazione
30 Dec 2014
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese

[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).Search in Google Scholar

[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).Search in Google Scholar

[3] B. Zerr, E. Bovio, B. Stage, Automatic mine classification approach based on AUV manoeuverability and cots side scan sonar, Proceedings of Goats 2001 Conference, La Spezia, Italy, (2001).Search in Google Scholar

[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).Search in Google Scholar

[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).Search in Google Scholar

[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).10.1364/AO.43.00023714735943Search in Google Scholar

[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).10.1049/ip-rsn:20040117Search in Google Scholar

[8] V. Myers, D. P. Williams, A POMDP for multiview target classification with an autonomous underwater vehicle, OCEANS, pp. 1-5, (2010).10.1109/OCEANS.2010.5664609Search in Google Scholar

[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)10.1109/JOE.2011.2175510Search in Google Scholar

[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 2009.10.1109/LGRS.2009.2021964Search in Google Scholar

[11] G. Dobeck, “Fusing sonar images for mine detection and classification,” Proc. SPIE—Int. Soc. Opt. Eng., vol. 3710, 1999, DOI: 10.1117/12.357082.10.1117/12.357082Search in Google Scholar

[12] J. Tucker, N. Klausner, and M. Azimi-Sadjadi, “Target detection in M-disparate sonar platforms using multichannel hypothesis testing,” in Proc. OCEANS Conf., Quebec City, QC, Canada, 2008, DOI: 10.1109/OCEANS.2008.5151818.10.1109/OCEANS.2008.5151818Search in Google Scholar

[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: 10.1117/12.445444.10.1117/12.445444Search in Google Scholar

[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 2004.10.1007/978-3-540-24775-3_35Search in Google Scholar

[15] Hall, D. L. and Steinberg, A.. Dirty Secrets in Multisensor Data Fusion, http://www.dtic.mil. (2001).10.21236/ADA394631Search in Google Scholar

[16] Blockeel, H., Page, D., Srinivasan, A.: Multiinstance tree learning. In: ICML (2005).10.1145/1102351.1102359Search in Google Scholar

[17] Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, (1993).Search in Google Scholar

[18] Hosmer, David W.; Lemeshow, Stanley. Applied Logistic Regression (2nd ed.). Wiley. (2000).10.1002/0471722146Search in Google Scholar

[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)Search in Google Scholar

[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)Search in Google Scholar

[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)10.1016/S0004-3702(96)00034-3Search in Google Scholar

[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)Search in Google Scholar

[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)Search in Google Scholar

[24] Schapire,R.E., Singer,Y.: Improved boosting algorithms using confidence-rated predictions. In: Machine Learning, 37 (3) 297–336 (1999)10.1023/A:1007614523901Search in Google Scholar

[25] Ting, K.M.: A Comparative Study of Cost-Sensitive Boosting Algorithms. In: Proc. Int’l Conf. Machine Learning, pp. 983-990 (2000)Search in Google Scholar

[26] Wang, J., Zucker, J.D.: Solving the multipleinstance problem: A lazy learning approach. In: ICML (2000)Search in Google Scholar

[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)Search in Google Scholar

[28] Zhang, Q., Goldman, S. A.: EM-DD: An improved multiple instance learning technique. In: Neural Information Processing Systems 14 (2001)Search in Google Scholar

[29] Elkan, C.: The Foundations of Cost-Sensitive Learning. In: Proc. Int’l Joint Conf. Artificial Intelligence, pp. 973-978 (2001)Search in Google Scholar

[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)10.1109/TKDE.2002.1000348Search in Google Scholar

[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)10.1613/jair.953Search in Google Scholar

[32] Zhang, M.L., Goldman, S.: Em-dd: An improved multi-instance learning technique. In: NIPS (2002)Search in Google Scholar

[33] Andrews, S., Tsochandaridis, I., Hofman, T.: Support vector machines for multiple instance learning. In: Adv. Neural. Inf. Process. Syst. 15, 561–568 (2003)Search in Google Scholar

[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)10.1145/1007730.1007735Search in Google Scholar

[35] Blockeel, H., Page, D., Srinivasan, A.: Multiinstance tree learning. In: ICML (2005)10.1145/1102351.1102359Search in Google Scholar

[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)10.1016/j.patcog.2007.04.009Search in Google Scholar

[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)10.1007/978-3-540-89378-3_29Search in Google Scholar

[38] Leistner, C., Saffari, A., and Bischof, H.: MIForests: Multiple Instance Learning with Randomized Trees. In: Proc. ECCV (2010)10.1007/978-3-642-15567-3_3Search in Google Scholar

[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)10.1007/978-3-642-25832-9_5Search in Google Scholar

[40] Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press (2011)10.1017/CBO9780511921803Search in Google Scholar

[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)10.1145/307400.307470Search in Google Scholar

[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)Search in Google Scholar

[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)Search in Google Scholar

[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)10.1145/1961189.1961199Search in Google Scholar

[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)Search in Google Scholar

[46] Bergstra, James; Bengio, Yoshua. :Random Search for Hyper-Parameter Optimization. In: J. Machine Learning Research 13: 281-305. (2012)Search in Google Scholar

[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)10.1109/ICMLA.2012.227Search in Google Scholar

[48] Wang, X., Matwin, S., Japkowicz, N., Liu, X.: Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets. In: Canadian Conference on AI (2013)10.1007/978-3-642-38457-8_15Search in Google Scholar

[49] Hosmer, David W. Lemeshow, Stanley: Applied Logistic Regression. Wiley. ISBN 0-471-35632-8.200010.1002/0471722146Search in Google Scholar

Articoli consigliati da Trend MD

Pianifica la tua conferenza remota con Sciendo