A Blind Domain Image Sample Classification Method Based on Overlimited Latent Feature Model

A technology of image samples and classification methods, applied in biological neural network models, character and pattern recognition, instruments, etc., can solve problems such as performance discounts of adaptive models, achieve the effects of reducing information loss, improving classification accuracy, and reducing domain deviations

Active Publication Date: 2022-03-29
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

When the amount of data in the target domain is seriously insufficient, the performance of the existing domain adaptation model will be greatly reduced

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  • A Blind Domain Image Sample Classification Method Based on Overlimited Latent Feature Model
  • A Blind Domain Image Sample Classification Method Based on Overlimited Latent Feature Model
  • A Blind Domain Image Sample Classification Method Based on Overlimited Latent Feature Model

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[0032] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the invention.

[0033] This embodiment provides a blind domain image sample classification method based on an over-limit latent feature model, please refer to figure 1 shown, including the following steps:

[0034] S1: In the training stage of the source domain ultra-limited hidden feature model, obtain the source domain image data matrix for model training, the corresponding label matrix, and the hidden layer output matrix output by the hidden nodes of the ultra-limited learning machine according to the image data matrix .

[0035] Specifically, the image data set used for source domain mo...

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Abstract

The invention discloses a blind field image sample classification method based on an over-limit latent feature model. The method adopts a cross-field data generation and augmentation strategy to efficiently reduce deviations between different data fields. Specifically, the ultra-limited latent feature model incorporates ELM and ELM‑AE (ELM-based autoencoder) into a unified optimization model, inherits the advantages of ELM and ELM‑AE, and can obtain Good image classification and image reconstruction effects, with image classification and image reconstruction capabilities, and can better reveal the potential relationship between original image data and advanced semantics, reduce information loss, and improve image classification accuracy. The invention solves the blind field adaptation problem by using the over-limit hidden feature model, integrates cross-field knowledge, effectively reduces the field deviation between data in different fields, and improves the classification accuracy of blind field data.

Description

technical field [0001] The present invention relates to the technical field of image classification and intelligent optimization, and more specifically, relates to a blind field image sample classification method based on an over-limit hidden feature model. Background technique [0002] In the field of computer vision, people often collect data from multiple fields, and there are differences in distribution. Traditional machine learning methods assume that the training set and the test set obey the same distribution. This assumption is often difficult to be satisfied in reality. Cross-domain image recognition is currently a research hotspot in the field of computer vision. Domain adaptation is to solve the problem of cross-domain image recognition. It is One of the research contents of transfer learning focuses on solving the problem that the feature space is consistent, the category space is consistent, and only the feature distribution is inconsistent. [0003] The basic ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/764G06K9/62G06N3/04
Inventor 郭坦张磊谭晓衡杨柳鲁银芝梁志芳胡昊
Owner CHONGQING UNIV OF POSTS & TELECOMM
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