Parallel AdaBoost feature extraction method of multi-core clustered system

A cluster system and feature extraction technology, applied in multi-programming devices, instruments, character and pattern recognition, etc., can solve problems such as difficulty in giving full play to the performance advantages of multi-core cluster systems, inability to fully utilize the parallel processing capabilities of multi-core cluster systems, etc. The effect of the feature extraction process

Inactive Publication Date: 2011-10-26
HUNAN CHUANGYUAN INTELLIGENT TECH
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Problems solved by technology

However, the traditional AdaBoost algorithm is a serial training algorithm, which cannot make full use of the parallel processing capability of the multi-core cluster system, and it is difficult to take advantage of the performance advantages of the multi-core cluster system. This patent designs a parallel AdaBoost feature extraction for the multi-core cluster system to solve this problem. method

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  • Parallel AdaBoost feature extraction method of multi-core clustered system
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  • Parallel AdaBoost feature extraction method of multi-core clustered system

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[0030] In order to make the technical means, creative features, work flow, and use methods of the present invention achieve the purpose and effect easily understood, the present invention will be further described below in conjunction with specific embodiments.

[0031] Such as image 3 The flow chart of the parallel Adaboost training method of the multi-core cluster system. The entire parallel training process includes six major steps, namely, initializing feature sets and sample sets, feature set distribution, parallel evaluation of feature subsets, optimal feature collection of subsets, and global Optimal feature broadcasting and parallel sample weight updates. Assuming that a given multi-core cluster system includes computer nodes, and each computer node has a processor core, the following six steps are described in detail.

[0032] When initializing the feature set and the sample set, the feature set and the sample set need to be stored in the storage system of the multi...

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Abstract

The invention discloses a parallel AdaBoost feature extraction method of a multi-core clustered system. Each iterative process of an AdaBoost is reformed through the following method that: firstly, a feature set needed to be scanned in an AdaBoost training process is divided and uniformly distributed on each node of the multi-core clustered system; then, each computing node further divides the own distributed feature subset, different computing cores respectively scan the divided subset and collect the computing result of each computing core, so as to obtain an optimal feature in the feature subset; and finally, the clustered system specifies the optimal features in the feature subsets obtained by each node, thereby obtaining a global optimal feature. The method provided by the invention can make full use of parallel processing capability of the multi-core clustered system, thereby greatly accelerating the AdaBoost feature extraction process.

Description

technical field [0001] The invention relates to the fields of large-scale parallel computing and pattern recognition, and is a method for accelerating AdaBoost feature extraction by using a multi-core cluster system. Background technique [0002] The feature extraction method based on AdaBoost machine learning algorithm is one of the popular feature extraction methods at present. The Adaboost machine learning algorithm is an effective method to solve the two-class classification problem, such as determining whether a picture is a face picture. This method is based on a given sample set (including positive samples and negative samples) to the feature set of this type of sample ( Such as the Haar feature set) for screening, and then the screened features are weighted to form a classifier. The classifier obtained by this method has high accuracy and robustness, and is widely used in various pattern recognition systems, such as person detection, face detection, human eye detect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/66G06F9/46
Inventor 夏东
Owner HUNAN CHUANGYUAN INTELLIGENT TECH
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