Image Classification Method Based on Denoising Sparse Autoencoder and Density Spatial Sampling

A sparse autoencoder, spatial sampling technology, used in instrumentation, character and pattern recognition, computing, etc.

Active Publication Date: 2020-10-30
YANCHENG TEACHERS UNIV
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Problems solved by technology

[0003] The present invention provides an image classification method based on denoising sparse autoencoder and density space sampling, aiming at solving the problem of image feature extraction and encoding, overcoming the defects existing in existing image classification methods, reducing computing cost, and improving classification accuracy

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  • Image Classification Method Based on Denoising Sparse Autoencoder and Density Spatial Sampling
  • Image Classification Method Based on Denoising Sparse Autoencoder and Density Spatial Sampling
  • Image Classification Method Based on Denoising Sparse Autoencoder and Density Spatial Sampling

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[0042] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0043] The test experiment hardware and software environment of the present embodiment is as follows:

[0044] Hardware type:

[0045] Computer type: desktop;

[0046] CPU: Intel(R) Core(TM) i5-5200U CPU@2.20GHz

[0047] Memory: 8.00GB

[0048] System type: 64-bit operating system

[0049] Development language: Matlab

[0050] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings. The embodiment takes the STL-10 database as an example, the database contains 10 types of RGB images, and the size of each image is 96*96. The number of training samples used for supervised training is 5000 in total, and the 5000 training samples are divided into ten folds. The number of training samples used for supervised training each time is 1000, and the number of test samples is...

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Abstract

The invention discloses an image classification method based on denoising sparse automatic encoder and density space sampling. The steps are: construct a training set of image blocks; construct a single hidden layer noise reduction sparse autoencoder, input the image block training set, and train the noise reduction sparse autoencoder; The image is subjected to density space sampling; the noise reduction sparse autoencoder is used to extract local feature set information from the spatial region obtained by density space sampling of each image; the feature set information is encoded by two-layer stacked Fisher Vector to obtain each image The final Fisher vector; use the Fisher vector to train the classifier to achieve image classification. The invention can accurately acquire image information, improves the classification accuracy of images, and can be used in the construction of a large-scale image classification and retrieval system.

Description

technical field [0001] The invention belongs to the technical field of image classification, and particularly relates to an image classification method based on noise reduction sparse automatic encoder and density space sampling. Background technique [0002] With the development of multimedia technology, image classification has become a hot research topic in the field of computer vision. Image classification is to classify images into different preset categories according to certain attributes they have. How to express the image effectively is the key to improve the accuracy of image classification, among which the selection and extraction of features are the key and difficult problems in the current image classification. Traditional artificially designed feature methods such as Gabor filter, SIFT, LBP, HOG, etc., have achieved certain results in image classification, but these methods need to be carefully designed and cannot be well applied to specific problems. In rece...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/2413
Inventor 张辉
Owner YANCHENG TEACHERS UNIV
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