Car Make and Model Recognition using Sparse Coding and Bag of Features

In this paper we apply the bag of feature method to the car make and model recognition problem. By using a sparse coding technique we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply standard classification techniques to obtain some promising results.

[1]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[2]  Geoffrey E. Hinton.  Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[3]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[4]  Nick Pears,et al.  Automatic make and model recognition from frontal images of cars , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[5]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[6]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[7]  Tony Lindeberg,et al.  Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention , 1993, International Journal of Computer Vision.

[8]  Yifan Zhou,et al.  Vehicle Type and Make Recognition by Combined Features and Rotation Forest Ensemble , 2012, Int. J. Pattern Recognit. Artif. Intell..

[9]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[10]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[11]  Maurice Milgram,et al.  Reconnaissance par vision du type d'un véhicule automobile , 2009 .

[12]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[13]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[16]  Timothy F. Cootes,et al.  Analysis of Features for Rigid Structure Vehicle Type Recognition , 2004, BMVC.

[17]  Stefan Badura,et al.  Advanced scale-space, invariant, low detailed feature recognition from images - car brand recognition , 2010, Proceedings of the International Multiconference on Computer Science and Information Technology.

[18]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[19]  Attila Tanács,et al.  Car recognition from frontal images in mobile environment , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).

[20]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[21]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[22]  Tieniu Tan,et al.  Feature Coding in Image Classification: A Comprehensive Study , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Eleftherios Kayafas,et al.  Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme , 2010, IEEE Transactions on Intelligent Transportation Systems.

[24]  A Psyllos,et al.  Vehicle Authentication from Digital Image Measurements , 2008 .

[25]  Matthew Turk,et al.  Car-Rec: A real time car recognition system , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[26]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[27]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[28]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[29]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[30]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[31]  M. Saquib Sarfraz,et al.  A Probabilistic Framework for Patch based Vehicle Type Recognition , 2011, VISAPP.

[32]  Cordelia Schmid,et al.  Learning Object Representations for Visual Object Class Recognition , 2007, ICCV 2007.

[33]  Meena Abdelmaseeh,et al.  Car Make and Model recognition combining global and local cues , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[34]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[35]  Gérard G. Medioni,et al.  3-D model based vehicle recognition , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[36]  Vincent Lepetit,et al.  Are sparse representations really relevant for image classification? , 2011, CVPR 2011.

[37]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[38]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[39]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

[40]  Eran A. Edirisinghe,et al.  Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition , 2007, Electronic Imaging.

[41]  Yong Wang,et al.  An Efficient Vehicle Model Recognition Method , 2013, J. Softw..

[42]  Hamid Reza Pourreza,et al.  Vehicle Recognition Based on Fourier, Wavelet and Curvelet Transforms - a Comparative Study , 2007, Fourth International Conference on Information Technology (ITNG'07).

[43]  David Menotti,et al.  Towards an automatic vehicle access control system: License plate location , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[44]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..