Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
The nature of statistical learning theory
The nature of statistical learning theory
On the Error-Reject Trade-Off in Biometric Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Robust Real-Time Face Detection
International Journal of Computer Vision
Kernel Autoassociator with Applications to Visual Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Vision-Based Vehicle Identification System
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Pattern Analysis & Applications
Confidence-based classifier design
Pattern Recognition
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Haar Wavelets and Edge Orientation Histograms for On---Board Pedestrian Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Cascade Linear SVM for Object Detection
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
Classified Vector Quantisation and population decoding for pattern recognition
International Journal of Artificial Intelligence and Soft Computing
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Automatic traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems
A kernel autoassociator approach to pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Machine Vision and Applications
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Automatic vehicle classification is an important area of research for intelligent transportation, traffic surveillance and security. A working image-based vehicle classification system is proposed in this paper. The first component vehicle detection is implemented by applying histogram of oriented gradient features and SVM classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by a hybrid model composed of clustering and kernel autoassociator (KAA). The KAA model is a generalization of auto-associative networks by training to recall the inputs through kernel subspace. As an effective one-class classification strategy, KAA has been proposed to implement classification with rejection, showing balanced error---rejection trade-off. With a large number of training samples, however, the training of KAA becomes problematic due to the difficulties involved with directly creating the kernel matrix. As a solution, a hybrid model consisting of self-organizing map (SOM) and KAM has been proposed to first acquire prototypes and then construct the KAA model, which has been proven efficient in internet intrusion detection. The hybrid model is further studied in this paper, with several clustering algorithms compared, including k-mean clustering, SOM and Neural Gas. Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the hybrid model. The proposed scheme offers a performance of accuracy over $$95~\%$$95% with a rejection rate $$8~\%$$8% and reliability over $$98~\%$$98% with a rejection rate of $$20~\%$$20%. This exhibits promising potentials for real-world applications.