Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Median filtering detection method based on PCA network

A technology of PCA network and detection method, which is applied in image data processing, instrument, character and pattern recognition, etc., can solve the problems of long model training time and low accuracy rate, and achieve the goal of avoiding network performance degradation, easier selection, and convenient implementation Effect

Active Publication Date: 2017-09-19
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0016] The purpose of the present invention is to overcome the technical defects of low accuracy and long model training time in the existing image median filter detection algorithm in compressed images and small-size image median filter detection, and propose a median filter based on PCA network Filter detection method, hereinafter referred to as "this method"

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Median filtering detection method based on PCA network
  • Median filtering detection method based on PCA network
  • Median filtering detection method based on PCA network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0064] This embodiment illustrates the specific implementation process of this method, such as figure 1 Shown.

[0065] From figure 1 It can be seen that the process of this method is:

[0066] Step ①: Select a picture database;

[0067] Specifically, this embodiment selects the BOSSBase database;

[0068] Step ②: Mark and cut the pictures to obtain the training set;

[0069] This step is the same as step 1 and step 2. Specifically in this embodiment, the picture is cut into image blocks with a size of 32×32;

[0070] Step ③: Calculate the median filter residual;

[0071] This step is the same as step 3, specifically in this example,

[0072] Step ④: Let k=1;

[0073] Step ⑤: Determine whether k is greater than M:

[0074] Among them, M represents the total number of image blocks in the training set;

[0075] If yes, skip to step ⑧;

[0076] If not, skip to step ⑥;

[0077] Step ⑥: Extract the picture features of the k-th picture;

[0078] The extraction method is the same as steps 4-10. Speci...

Embodiment 2

[0095] figure 2 The network model framework of this method, figure 2 It is a specific flow chart of applying this model for median filter detection. The algorithm will be described in detail below in conjunction with the accompanying drawings, but the specific implementation form of this method is not limited to this.

[0096] The specific training process of the model is as follows:

[0097] 1) Select 10,000 images, take out the 32×32 image at the center position from each image, and record its label as 0;

[0098] 2) Perform median filtering on the obtained small-size image, and record its label as 1, and the obtained image and the original image are used as the training set of the model;

[0099] 3) figure 2 The “MFR Filter” in the middle means calculating the input median filter residual and outputting it, specifically according to each input picture I i ,Calculate and output the residual value according to formula (1);

[0100] 4) Take the median residual of a picture as an exam...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a median filtering detection method based on a PCA network, and belongs to the information safety and digit image information processing technical field; the method comprises model building and target detection parts; the model building part comprises the following steps: 1, selecting an image and extracting materials; 2, filtering extracted material median so as to obtain the filtered material; 3, using the materials outputted by step 1and step 2 as a training set, and calculating a median filtering residual error; 4, building the PCA network; 5, training a support vector machine so as to obtain a trained model. The target detection part comprises the following steps: A, cutting a to-be-detected image into small size images; B, inputting small size images outputted by step A into the built model, determining whether median filtering treatment is finished or not according to the model output, and deciding whether to jump to step C; C, marking the image block with the median filtering treatment in A, and jumping to B. The method is simple in structure, short in training time, less in artificial setting parameters, suitable for various image sets, can be conveniently realized on a FPGA, thus greatly improving the operation speed.

Description

Technical field [0001] The invention relates to a median filter detection method based on a PCA network, which belongs to the technical fields of information security and digital image information processing. Background technique [0002] With the rapid development of communication technology and computer technology, people have fully entered the information age. In the information age, people can easily and quickly obtain a large amount of information through the Internet or other digital media, but at the same time, some valuable digital information is easily tampered with or misappropriated. In consideration of copyright protection and information security, the protection of original information is particularly important. In the field of digital imagery, the detection methods for judging whether information has been artificially modified can be collectively referred to as forensic analysis. In recent years, due to the increase in copyright and information security requirement...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/20G06K9/62
CPCG06T5/20G06T2207/20032G06F18/214G06F18/2411
Inventor 李炳照王贤
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com