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Category label recovery method for low-rank image feature analysis

A technology of category labels and image features, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of model underfitting, missing labels, and low recognition rate.

Pending Publication Date: 2020-06-12
DONGHUA UNIV
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AI Technical Summary

Problems solved by technology

[0003] Label information plays a vital role in LDA-based image classification, but the actual obtained datasets often have the problem of missing labels.
If this problem is ignored, the trained classification model may have various problems. If the sample images without labels are discarded, the model will be underfitted or the recognition rate will be low.

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  • Category label recovery method for low-rank image feature analysis
  • Category label recovery method for low-rank image feature analysis
  • Category label recovery method for low-rank image feature analysis

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Embodiment Construction

[0017] In order to make the present invention more obvious and understandable, the preferred embodiments are described in detail as follows in conjunction with the accompanying drawings:

[0018] Such as figure 1 As shown, first obtain all samples of the original sample set A, a total of n sample images, a total of C categories, in the c category there are N e samples, c ∈ {1, 2, ..., C}. The size of each image is S×S pixels, and the S×S-dimensional image matrix is ​​pulled into an S 2 dimensional column vector, n images form a S 2 ×n-dimensional sample matrix x=[x 1 , x 2 ,...,x n ], the label y corresponding to each image 1 ,...y n ∈ {1, 2, ..., C}, corresponding to the original label matrix Y = [y 1 ,y 2 ,...,y n ]. For the above-mentioned original sample set A, the present invention provides a method for recovering class labels for low-rank image feature analysis, comprising the following steps:

[0019] Step 1: Generate u non-repetitive random numbers, and in ...

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Abstract

The invention relates to a category label recovery method for reliable image classification, and belongs to the technical field of machine vision. The invention discloses a category label recovery method for low-rank image feature analysis by combining a label propagation process, PCA dimension reduction and LDA classification. According to the method, a label propagation process is introduced toobtain a data set after label recovery, then PCA reliable dimensionality reduction is executed to reduce the dimensionality of a reliable data set, meanwhile, information with the largest contributionto variance in the reliable data set is kept, finally, LDA features are extracted, and low-rank features for discriminant analysis are obtained. According to the method, a data set containing missinglabels is preprocessed, the missing category labels are restored, and the most discriminative feature is extracted through reliable dimension reduction, so that a subsequent nearest neighbor classifier is more accurate and reliable; and the robustness of the sample label data is improved, so that the classification model is more effective.

Description

technical field [0001] The invention relates to a category label recovery method for low-rank image feature analysis, which belongs to the technical field of machine vision. Background technique [0002] Linear discriminant analysis (LDA) can use the known category labels of sample images to find the projection subspace that is most helpful for image classification, which belongs to a supervised machine learning method. LDA extracts the most discriminative low-rank features from high-dimensional data. These features help to gather all samples of the same category and separate samples of different categories as much as possible. The feature with the largest in-divergence ratio. Efficient preprocessing of image datasets is quite difficult. Generally, an effective method is to keep the main information contained in the dataset as much as possible and reduce its dimensions. Typically, principal component analysis (PCA) can perform effective dimensionality reduction analysis on...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/213G06F18/24147G06F18/2415
Inventor 时庭庭刘浩应晓清王凯巡沈港黄震廖荣生魏国林魏冬周健田伟
Owner DONGHUA UNIV
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