Bootstrapping weak learning method based on random fern and classifier thereof

A weak classifier, random fern technology, applied in the field of machine learning, can solve problems such as low computational efficiency, low accuracy, slow convergence speed, etc.

Inactive Publication Date: 2012-10-03
SOUTHWEST JIAOTONG UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, these weak learning methods either converge slowly, are not accurate enough, or are computationally inefficient

Method used

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  • Bootstrapping weak learning method based on random fern and classifier thereof
  • Bootstrapping weak learning method based on random fern and classifier thereof
  • Bootstrapping weak learning method based on random fern and classifier thereof

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Embodiment Construction

[0055] The method of the present invention is suitable for both offline and online bootstrap classifier training, and can be used for various pattern recognition and computer vision problems, including video object tracking, medical image analysis, optical character recognition, handwriting recognition, face recognition, fingerprint recognition , document classification, photogrammetry and remote sensing, etc.

[0056] Taking video object tracking as an example: at the initial frame time of tracking, the corresponding positive and negative samples are extracted from the initially obtained target position and its surrounding positions, and the classifier is trained according to the method proposed by the present invention. In the tracking process, in the search area centered on the target position determined last time, the classifier obtained from this training is used to classify and evaluate each position in the search area, and the position with the highest classification eva...

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Abstract

The invention provides a bootstrapping weak learning method based on a random fern and a classifier thereof, which belong to the technical field of computer graphic identification. In graphic identification, positive and negative patterns are generally judged by adopting the mean value distances of weighting and Gaussian probability distribution of a weak classifier. Or classification trees are taken as weak learners, nodes are partitioned by using the partitioning rule of error metric reduction maximization, and the weak classifiers are improved into strong classifiers. However, the weak learning methods have the defects of low convergence rate, poor accuracy and low computing efficiency. Through steps for selecting image characteristics, constructing a random fern and a weak learning method based on a random fern, establishing a weak classifier and a result classifier and the like, the image mode identification problems of complexity of an imaging environment and strict requirement on the operand can be well solved, a rapidly-converging and efficient bootstrapping weak learning method is realized, and a high-accuracy classifier for real-time processing is obtained. The method and the classifier are mainly applied to identification occasions in various modes.

Description

technical field [0001] The invention belongs to the technical field of computer graphic image pattern recognition, in particular to machine learning and computer vision technology. Background technique [0002] With the development of computer technology, it becomes possible to study complex information processing. An important form of information processing is pattern recognition, that is, the subject's recognition of the environment and objects, and the classification process is the basic task of pattern recognition. At present, the Boosting bootstrap classification method (here mainly refers to the AdaBoost adaptive bootstrap method) is more and more widely used in pattern recognition and machine learning tasks due to its practical application ability in detection and recognition, such as medical image analysis , optical text recognition, speech recognition, handwriting recognition, face recognition, fingerprint recognition, iris recognition, document classification, pho...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 权伟陈锦雄余南阳刘彬
Owner SOUTHWEST JIAOTONG UNIV
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