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

Abstract 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.

[1]  Jun Wang,et al.  Solving the Multiple-Instance Problem: A Lazy Learning Approach , 2000, ICML.

[2]  Kai Ming Ting,et al.  An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .

[3]  Eibe Frank,et al.  Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-Instance Data , 2011, Australasian Conference on Artificial Intelligence.

[4]  Benoît Zerr,et al.  Three-dimensional reconstruction of underwater objects from a sequence of sonar images , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[5]  Mohammad H. Poursaeidi,et al.  Robust support vector machines for multiple instance learning , 2012, Annals of Operations Research.

[6]  Horst Bischof,et al.  MIForests: Multiple-Instance Learning with Randomized Trees , 2010, ECCV.

[7]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[8]  Nello Cristianini,et al.  Controlling the Sensitivity of Support Vector Machines , 1999 .

[9]  Gerald J. Dobeck.  Fusing sonar images for mine detection and classification , 1999, Defense, Security, and Sensing.

[10]  Stanley Lemeshow,et al.  Applied Logistic Regression, Second Edition , 1989 .

[11]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[12]  David P. Williams,et al.  Adaptive Multiview Target Classification in Synthetic Aperture Sonar Images Using a Partially Observable Markov Decision Process , 2012, IEEE Journal of Oceanic Engineering.

[13]  Kai Ming Ting,et al.  A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.

[14]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[15]  Mohak Shah,et al.  Evaluating Learning Algorithms: A Classification Perspective , 2011 .

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Ashwin Srinivasan,et al.  Multi-instance tree learning , 2005, ICML.

[18]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[19]  Stan Matwin,et al.  Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection , 2012, 2012 11th International Conference on Machine Learning and Applications.

[20]  Scott Reed,et al.  Model-based approach to the detection and classification of mines in sidescan sonar. , 2004, Applied optics.

[21]  David P. Williams,et al.  Mine Classification With Imbalanced Data , 2009, IEEE Geoscience and Remote Sensing Letters.

[22]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[23]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[24]  James R. Foulds,et al.  Revisiting Multiple-Instance Learning Via Embedded Instance Selection , 2008, Australasian Conference on Artificial Intelligence.

[25]  Yvan Petillot,et al.  Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information , 2004 .

[26]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[27]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[28]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[29]  David L. Hall,et al.  Dirty Secrets in Multisensor Data Fusion , 2001 .

[30]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[31]  V Myers,et al.  A POMDP for multi-view target classification with an autonomous underwater vehicle , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[32]  Mahmood R. Azimi-Sadjadi,et al.  Adaptive underwater target classification with multi-aspect decision feedback , 2001, SPIE Defense + Commercial Sensing.

[33]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[34]  M.R. Azimi-Sadjadi,et al.  Target detection in M-disparate sonar platforms using multichannel hypothesis testing , 2008, OCEANS 2008.

[35]  Katharina Morik,et al.  Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring , 1999, ICML.

[36]  David Hopkin,et al.  Multiaspect Classification of Sidescan Sonar Images: Four Different Approaches to Fusing Single-Aspect Information , 2010, IEEE Journal of Oceanic Engineering.

[37]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[38]  N. Japkowicz.  Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .

[39]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[40]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[41]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[42]  Stan Matwin,et al.  Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets , 2013, Canadian Conference on AI.

[43]  Qi Zhang,et al.  EM-DD: An Improved Multiple-Instance Learning Technique , 2001, NIPS.

[44]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[45]  Xin Xu,et al.  Logistic Regression and Boosting for Labeled Bags of Instances , 2004, PAKDD.

[46]  Nello Cristianini,et al.  Further results on the margin distribution , 1999, COLT '99.