Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Selection of Histograms of Oriented Gradients Features for Pedestrian Detection
Neural Information Processing
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We present an example-based algorithm for detecting objects in images by integrating component-based classifiers, which automaticaly select the best feature for each classifier and are combined according to the AdaBoost algorithm. The system employs a soft-margin SVM for the base learner, which is trained for all features and the optimal feature is selected at each stage of boosting. We employed two features such as a histogram-equalization and an edge feature in our experiment. The proposed method was applied to the MIT CBCL pedestrian image database, and 100 sub-regions were extracted from each image as local-features. The experimental results showed fairly good classification ratio with selecting sub-regions, while some improvement attained by combining the two features, histogram-equalization and edge. However, the combination of features could to select good local-features for base learners.