Secret level marking identification method based on Krawtchouk moment and KNN-SMO classifier

A recognition method and classifier technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that the classified identification cannot be effectively recognized

Active Publication Date: 2016-04-20
深圳元物质科技集团有限公司
View PDF3 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Aiming at the fact that the usual text recognition algorithm cannot effectively identify the classified mark after being attacked, a secret mark recognition method based on Krawtchouk moment and KNN-SMO classifier is proposed, and the characteristics of the image can be well described by using the low-order Krawtchouk moment ...

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
  • Secret level marking identification method based on Krawtchouk moment and KNN-SMO classifier
  • Secret level marking identification method based on Krawtchouk moment and KNN-SMO classifier
  • Secret level marking identification method based on Krawtchouk moment and KNN-SMO classifier

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] The scheme of the present invention is specifically described below in conjunction with the accompanying drawings:

[0050] [1] Different attacks (including affine transformation, JPEG compression, brightness reduction, fuzzy processing, median filtering, mean filtering, contrast enhancement, etc.) are carried out on the classified mark to obtain experimental data.

[0051] [2] Divide the experimental data into two parts: training samples and test samples, which do not contain each other.

[0052] [3] Preprocessing training samples, including image grayscale, image inversion and binarization, image denoising, tilt correction, line word segmentation, thinning and normalization and other steps.

[0053] [4] Calculate the low-order Krawtchouk moments of the training samples after preprocessing as training features.

[0054] [5] To construct a KNN-SMO classifier, first use the KNN algorithm to prune the training set, determine the choice according to the similarities and d...

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 secret level marking identification method based on a Krawtchouk moment and a KNN-SMO classifier. The method relates to a theory based on the Krawtchouk moment and the KNN-SMO classifier applied to the secret level marking identification of the computer forensics, and the method comprises the steps: a secret level marking image is performed the image preprocessing, the feature vector is formed by calculated Krawtchouk moments of the image, and the secret level marking image is performed the classification and identification by the KNN-SMO classifier. On one side, the low-stage Krawtchouk moment can be used to express the characteristic of the image well and has good stability under the common attack, on the other side, the KNN-SMO classifier is used, so that the classifier has KNN quick sorting capacity and SMO advantage of solving small sample problem, so that precision and speed of the secret level marking identification is improved.

Description

technical field [0001] The invention belongs to the field of electronic forensics, and in particular relates to a secret-level sign recognition method based on Krawtchouk moments and a KNN-SMO classifier. Background technique [0002] In electronic forensics, after completing the retrieval of secret-related documents, it is necessary to further identify the secret-level identification of the secret-related documents, and record the results of the inspection to form a log as evidence for forensics. [0003] Existing recognition methods, common methods include template matching method (AravindaCV, PrakashHN. Template matching method for Kannada Handwritten recognition based on correlation analysis [C] / / Contemporary Computing and Informatics (IC3I), 2014International Conference on. IEEE, 2014:857-861.) [0004] , Character feature statistics method (DasS, jyotiChoudhuryS, DasAK, etal.SelectionofGraph-BasedFeaturesforCharacterRecognitionUsingSimilarityBasedFeatureDependencyandRo...

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
IPC IPC(8): G06K9/62
CPCG06V10/758G06F18/24147
Inventor 傅德胜经正俊
Owner 深圳元物质科技集团有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap