Mode training method based on ensemble learning and mode indentifying method

A technology that integrates learning and training methods, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of not increasing the income of training sample sets, unable to guarantee sparsity, etc., to improve training efficiency and detection efficiency , the effect of high recognition performance

Inactive Publication Date: 2012-06-27
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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[0003] In 2009, M.Enzweiler and D.M.Gavrila published the article "Monocular Pedestrian Detection: Survey and Experiments" on pages 2179-2195 of IEEE Transactions on Pattern Analysis and Machine Intelligence through the study of pedestrian detection in images. The combination of feature and pattern cla

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  • Mode training method based on ensemble learning and mode indentifying method
  • Mode training method based on ensemble learning and mode indentifying method
  • Mode training method based on ensemble learning and mode indentifying method

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[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the pattern training and recognition method based on integrated learning according to an embodiment of the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0025] The present invention makes full use of the flexibility and sparse characteristics of sparse coding to perform pattern training and recognition, and the sparse coding will be briefly described below.

[0026] Signal sparse coding (Sparse Coding) or sparse representation (Sparse Representations) based on redundant dictionaries is a new signal representation theory, which uses an over-complete redundant function system (redundant dictionary) to replace traditional orthogonal basis functions, such as The dat...

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Abstract

The invention provides a mode training method based on ensemble learning and a mode indentifying method. The mode training method comprises the following steps of: 1) carrying out dictionary learning on training samples to generate a redundant dictionary; 2) utilizing the redundant dictionary to carry out sparse encoding on the training samples to obtain a sparse encoding coefficient of each training sample; 3) carrying out sparse subspace division on all the training samples according to the sparse encoding coefficients; and 4) carrying out sub-model training on the training sample in each sparse subspace to obtain a sub-model for classifying. According to the mode training method based on the ensemble learning and the mode indentifying method, provided by the invention, higher indentifying performance can be obtained; and meanwhile, the training efficiency and the detection efficiency can be obviously improved.

Description

technical field [0001] The present invention relates to the field of intelligent systems, and more particularly, to the fields of pattern recognition and machine learning. Background technique [0002] For data with high dimensionality, the detection and recognition models trained by traditional methods on small data sets are difficult to cover all possible sample situations, and the generalization performance and detection accuracy are poor on open data sets. Especially for the rapidly growing image and video data on the Internet, not only has high feature dimension, but also has the characteristics of wide coverage, diverse content, and fast update. This kind of possible sample can improve the detection accuracy of the algorithm on the open multimedia data set. [0003] In 2009, M.Enzweiler and D.M.Gavrila published the article "Monocular Pedestrian Detection: Survey and Experiments" on pages 2179-2195 of IEEE Transactions on Pattern Analysis and Machine Intelligence thro...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 唐胜韩淇张勇东李锦涛
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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