Description: AdaBoost is an efficient tool in machine learning. It can combine a series of weak learners into a strong learner. Besides pattern classification, it also can be applied into feature selection. This document explains the use of AdaBoost. Platform: |
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Author:njustyw |
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Description: adaboost FEATURE SELECTION USING ADABOOST FOR FACE EXPRESSION RECOGNITION-FEATURE SELECTION USING ADABOOST FOR FACE EXPRESSION RECOGNITION Platform: |
Size: 167936 |
Author:丁云 |
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Description: Very recently tracking was approached using classification techniques such
as support vector machines. The object to be tracked is discriminated by a
classifier from the background. In a similar spirit we propose a novel on-line
AdaBoost feature selection algorithm for tracking. The distinct advantage of
our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object
(e.g. out of plane rotations, illumination changes) are handled quite naturally.
Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using
fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences. Platform: |
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Author:lili |
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Description: this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.-this codes is Source code for face detection of viola paper.of its Features is:
Feature Computation: The “Integral” image representation
Feature Selection: The AdaBoost training algorithm .
Real-timeliness: A cascade of classifiers.
Platform: |
Size: 420864 |
Author:fatemeh |
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Description: This paper presents an online feature selection algorithm
for video object tracking. Using the object and background
pixels from the previous frame as training samples, we model the
feature selection problem as finding a good subset of features to
better classify object from background in current frame. Platform: |
Size: 452608 |
Author:SAINATH1 |
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Search - Adaboost for feature selection