Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Preceding vehicle recognition based on learning from sample images
IEEE Transactions on Intelligent Transportation Systems
On-road vehicle detection using evolutionary Gabor filter optimization
IEEE Transactions on Intelligent Transportation Systems
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
An integrative approach to accurate vehicle logo detection
Journal of Electrical and Computer Engineering
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The identification of the make and model of vehicles from images captured by surveillance camera, also referred to as vehicle type recognition, is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we first comparatively studied two feature extraction methods for image description, i.e., the MPEG-7 edge orientation histogram (EOH) and the pyramid histogram of oriented gradients (PHOGs). EOH captures the spatial distribution of edges by detecting five predefined types of edge directions. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantised into a number of bins. Compared with previously proposed feature extraction approaches for vehicle recognition, EOH has the advantage of small feature size, economic calculation cost and relative good performance and PHOG has the ascendency in its description of more discriminating information. A composite feature description from PHOG and EOH can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for vehicle classification based on the combined features. A base classifier is trained with a randomly sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Experimental results using more than 600 images from 21 types show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With moderate ensemble size 30, the random subspace ensembles offers a classification rate close to 94%, showing the promising potential in real applications.