Target recognition for small samples of ladar range image using classifier ensembles

The range image has received considerable attention in the automatic target recognition field; however, a mass of range images are generally difficult to be collected in a real application. Therefore, with small samples of laser radar (ladar) range images, classifier ensembles-support vector machine (SVM) ensembles and back propagation neural networks (BPNN) ensembles-are applied to improve the performance of target recognition in this paper. There are three aspects of experiments. First, the performances of SVM and BPNN are compared with different numbers of training sets. Secondly, the SVM ensembles and the BPNN ensembles are applied to improve the performance. Thirdly, the performances of the SVM ensembles and the BPNN ensembles are analyzed with the view angle of the tested samples changing while the view angle of the trained samples is invariant. The experimental results demonstrate that the recognition rate is effectively improved by classifier ensembles. The SVM is superior to the BPNN when the number of the training sets is not less than the feature dimensions; however, the BPNN has a better approximating ability when the number of training sets is small and lower than feature dimensions.

[1]  S. Sathiya Keerthi,et al.  Parallel sequential minimal optimization for the training of support vector machines , 2006, IEEE Trans. Neural Networks.

[2]  Jeffrey H. Shapiro,et al.  Detecting objects in three-dimensional laser radar range images , 1994 .

[3]  Roman M. Balabin,et al.  Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. , 2011, The Analyst.

[4]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Majid Moavenian,et al.  A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification , 2010, Expert Syst. Appl..

[6]  Jeffrey H. Shapiro,et al.  Maximum-likelihood multiresolution laser radar range imaging , 1997, IEEE Trans. Image Process..

[7]  Lorenzo Bruzzone,et al.  A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Nikos Koutsias,et al.  SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[10]  Giles M. Foody,et al.  Feature Selection for Classification of Hyperspectral Data by SVM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Hong-Bo Xie,et al.  Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models. , 2009, Medical engineering & physics.

[13]  Wolfram Burgard,et al.  Robust place recognition for 3D range data based on point features , 2010, 2010 IEEE International Conference on Robotics and Automation.

[14]  Alireza Khotanzad,et al.  Recognition and pose estimation of unoccluded three-dimensional objects from a two-dimensional perspective view by banks of neural networks , 1996, IEEE Trans. Neural Networks.

[15]  Michael I. Miller,et al.  Hilbert-Schmidt Lower Bounds for Estimators on Matrix Lie Groups for ATR , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hyun-Chul Kim,et al.  Constructing support vector machine ensemble , 2003, Pattern Recognit..

[17]  Hui Chen,et al.  3D free-form object recognition in range images using local surface patches , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[18]  Rama Chellappa,et al.  Structure From Planar Motion , 2006, IEEE Transactions on Image Processing.

[19]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem☆ , 2008 .

[20]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[21]  Bijan G. Mobasseri,et al.  Robust Through-the-Wall Radar Image Classification Using a Target-Model Alignment Procedure , 2012, IEEE Transactions on Image Processing.

[22]  Qi Wang,et al.  Rotation-invariant target recognition in LADAR range imagery using model matching approach. , 2010, Optics express.

[23]  Jeffrey H. Shapiro,et al.  Laser Radar Target Detection With A Multipixel Joint Range-Intensity Processor , 1989, Other Conferences.

[24]  Jianbo Yang,et al.  An Improved Algorithm for the Solution of the Regularization Path of Support Vector Machine , 2010, IEEE Transactions on Neural Networks.

[25]  Jeffrey H. Shapiro,et al.  Maximum-likelihood laser radar range profiling with the expectation-maximization algorithm , 1992 .

[26]  Antonio Adán,et al.  3D scene retrieval and recognition with Depth Gradient Images , 2011, Pattern Recognit. Lett..

[27]  Leonidas J. Guibas,et al.  Probabilistic fingerprints for shapes , 2006, SGP '06.

[28]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[29]  Ching Y. Suen,et al.  A novel hybrid CNN-SVM classifier for recognizing handwritten digits , 2012, Pattern Recognit..

[30]  Chin Seng Chua,et al.  Point Signatures: A New Representation for 3D Object Recognition , 1997, International Journal of Computer Vision.

[31]  M.Y. Mashor,et al.  3D object recognition system using multiple views and cascaded multilayered perceptron network , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[32]  Mohammed Bennamoun,et al.  Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Juan José Rodríguez Diez,et al.  Random Subspace Ensembles for fMRI Classification , 2010, IEEE Transactions on Medical Imaging.

[34]  Jacques Verly,et al.  Model-based automatic target recognition (ATR) system for forwardlooking groundbased and airborne imaging laser radars (LADAR) , 1996, Proc. IEEE.

[35]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[36]  Peng Zhang,et al.  Neural-network-based single-frame detection of dim spot target in infrared images , 2007 .

[37]  Qi Li,et al.  Range image noise suppression in laser imaging system , 2009 .

[38]  Sheng-Hsun Hsu,et al.  Application of SVM and ANN for image retrieval , 2006, Eur. J. Oper. Res..