Image classification and reconstruction method based on extreme implicit feature learning model

A technology of learning model and ultra-limited learning machine, which is applied in the field of image classification and reconstruction based on the ultra-limited latent feature learning model, which can solve problems such as rigidity and difficulty in effectively exploring the potential relationship between original observation data and advanced semantics

Active Publication Date: 2019-06-25
CHONGQING UNIV OF POSTS & TELECOMM
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This strategy may be too rigid to effectively explore the underl...

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  • Image classification and reconstruction method based on extreme implicit feature learning model
  • Image classification and reconstruction method based on extreme implicit feature learning model
  • Image classification and reconstruction method based on extreme implicit feature learning model

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[0035] 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.

[0036] This embodiment provides an image classification and reconstruction method based on an over-limit latent feature learning model, including the following steps:

[0037] S1: training phase, specifically, to obtain the image data set for model training The number of samples is N, the dimension is d, and the hidden layer input weight vector w i and hidden layer node offset b i , perform random assignment, its size range is [-1,+1], input the training sample set into the input layer, and the number of hidden layer nodes is L. Get the hidden layer output matrix The h...

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Abstract

The invention discloses an image classification and reconstruction method based on an extreme implicit feature learning model, and the method employs a data reconstruction strategy to carry out the feature learning, and efficiently learns the original perception data and a corresponding high-level semantic conversion relation. In particular, ELM and ELM-AE (an auto-encoder based on ELM) are unified in one learning model, the model has image classification capability and image reconstruction capability, the potential relationship between original image data and advanced semantics can be betterdisclosed, information loss is reduced, image classification precision is improved, and the model has excellent image data reconstruction capability. ELF (Extreme Limited Hidden Feature Learning) inherits advantages of the ELM and ELM-AE, good image classification and image reconstruction effects can be obtained under the condition that the original data information is protected. In addition, an efficient algorithm based on a staggered direction method is used for solving and optimizing the ELF model, and the precision of the ELF model is further improved.

Description

technical field [0001] The invention relates to the technical field of image classification and intelligent optimization, and more specifically, relates to an image classification and reconstruction method based on an over-limit hidden feature learning model. Background technique [0002] In the field of machine learning and computer vision, extracting discriminative and compact representations of images can effectively reveal potential important information hidden in data, and can seamlessly connect high-level semantic data with original data. The performance of machine learning models largely depends on Because of the features used, it has received great attention from researchers. Recent studies have demonstrated that multi-layer neural networks can learn multi-layer abstract features from data, which can significantly improve the performance of machine vision models. As an efficient learning algorithm for a single hidden layer feed-forward neural network, ELM (Extreme L...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
Inventor 郭坦胡昊谭晓衡杨柳梁志芳熊炼
Owner CHONGQING UNIV OF POSTS & TELECOMM
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