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Probability linear discriminant analysis image classification method based on L1 bound norm

A technology of linear discriminant analysis and L1 norm, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as image outliers

Inactive Publication Date: 2017-08-11
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an image classification method based on L1 norm probabilistic linear discriminant analysis, which can solve the problem of abnormal values ​​in the image

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  • Probability linear discriminant analysis image classification method based on L1 bound norm
  • Probability linear discriminant analysis image classification method based on L1 bound norm
  • Probability linear discriminant analysis image classification method based on L1 bound norm

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Embodiment 1

[0098] The experimental results of the image classification method of the present invention are as follows:

[0099] 1. Outlier detection results of L1-PLDA

[0100] For the face database, take the Yale database as an example to illustrate the detection function of β; figure 2 It can be seen from the experimental results in, that when there is a block in the face, the image showing β can accurately find the position of the block.

[0101] 2. Classification results of L1-PLDA

[0102] The present invention has applied the L1-PLDA algorithm to the face database for classification, and has achieved obvious effects. Take Yale library and Feret library as examples to illustrate the effect of classification.

[0103] 1) There are 15 categories in the Yale library, and each person has 11 pictures. In the experiment, the present invention uses 8 training and 3 tests. In the training set, there are 2 pictures of each category with occlusion blocks; the images are unified as 64×64 Grayscale im...

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Abstract

The invention discloses a probability linear discriminant analysis image classification method based on L1 bound norm and solves a problem of an abnormal value existing in an image. Different from a traditional PLDA, Laplace distribution is employed to describe noise, Laplace is a probability density function based on the L1-bound norm, so an error value can be prevented from being amplified; through introducing a hidden variable, parameters of a variational expectation maximization solution model and a dimension reduction matrix are utilized; the dimension reduction matrix is taken as characteristics of a sample, the L1-bound norm is utilized in the model to realize error description, the solved dimension matrix is closer to a main direction, and the image classification effect can be improved.

Description

Technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to an image classification method based on probabilistic linear discriminant analysis of the L1 norm, and is particularly suitable for image classification with abnormal values ​​in the image. Background technique [0002] In image processing, the image is often vectorized into a high-dimensional data. However, high-dimensional data is often evenly distributed in a low-dimensional space or popular space. Therefore, finding the mapping relationship between high-dimensional data and low-dimensional space has become an important issue for image classification. In recent decades, data dimensionality reduction algorithms have been deeply studied. Linear Discriminant Analysis (LDA) is a dimensionality reduction method widely used in image classification. LDA uses a projection matrix to map high-dimensional data to a low-dimensional space, so that the ratio of the mapped...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2132G06F18/2415
Inventor 丁文鹏胡向杰孙艳丰胡永利
Owner BEIJING UNIV OF TECH
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