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A method for improving the classification accuracy of a vector graph bitmap

A precision and vector graphics technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of low accuracy, inaccurate classification, wrong classification, etc., and achieve the effect of improving precision and classification accuracy

Pending Publication Date: 2019-06-28
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1. The method of suffix classification is not accurate;
[0006] 2. Under the premise of a large number of printed products, traditional detection is prone to a large number of misclassifications, and the accuracy rate is very low

Method used

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  • A method for improving the classification accuracy of a vector graph bitmap
  • A method for improving the classification accuracy of a vector graph bitmap
  • A method for improving the classification accuracy of a vector graph bitmap

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] An embodiment of the present invention provides a method for improving the classification accuracy of vector graphics bitmaps, see figure 1 , the method includes the following steps:

[0037] 101: Obtain the number of element paths of the classification diagram and the size of the classification diagram by reading the binary byte stream of each classification diagram;

[0038] In the specific implementation, the number of element paths and the size of the classification map are put into a two-dimensional feature vector as labels, that is, each two-dimensional feature vector is composed of the element path and the size of the classification map. The feature vector is converted into an n*2 matrix, which is used as the input matrix of the classification map.

[0039] 102: Divide the input matrix into training set, test set and verification set according to the ratio of 7:2:1, use the input matrix to determine the stationary point of minimizing the objective function, and ...

Embodiment 2

[0050] Combined with the specific calculation formula, figure 1 The scheme in Example 1 is further introduced, see the following description for details:

[0051] 201: Perform two-dimensional feature vector extraction on samples composed of batch classification graphs, mainly for obtaining the number of element paths;

[0052] Among them, in a vector map or bitmap, there are 0-50000 element paths. The attributes of these paths are different, and the image features represented are also different. If multi-dimensional path features are added, the learning effect is very poor at this time. The embodiment of the present invention performs dimensionality reduction processing on many features, and only considers the one-dimensional feature of the number of element paths in different classification graphs.

[0053] In actual implementation, the size of the classification map is also a relatively important feature of the classification map. If the size of the classification map is us...

Embodiment 3

[0071] Below in conjunction with concrete example, table 1, figure 2 1. Carry out feasibility verification to the scheme in embodiment 1 and 2, see the following description for details:

[0072] The embodiment of the present invention obtains the number of element paths of the classification diagram through classification diagram analysis, and obtains the size of the classification diagram, puts two labels into a two-dimensional feature vector, and uses several feature vectors to form a sample matrix as an input matrix for clustering Learn.

[0073] The purpose of adopting cluster analysis in the embodiment of the present invention is to analyze whether the data belong to each independent group, so that members in one group are similar to each other but different from members in other groups. It analyzes a collection of data objects, but unlike classification analysis, the divided classes are unknown. Therefore, cluster analysis is also called unsupervised or unsupervised (...

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Abstract

The invention discloses a method for improving the classification accuracy of a vector graph bitmap. The method comprises the following steps: obtaining the number of element paths of a classificationgraph and the size of the classification graph by reading a binary byte stream of each classification graph; Dividing the input matrix into a training set, a test set and a verification set accordingto a ratio of 7: 2: 1, determining a residence point of a minimized objective function by using the input matrix, and determining classification center points of the two types of classification graphs; obtaining clustering classification center by minimizing the objective function, obtaining and marking then the two types of classification graphs respectively, and achieving rapid and accurate classification. According to the method, the clustering model is trained by introducing the element paths and the classification graph size tags, and the classification graph classification accuracy canbe effectively improved.

Description

technical field [0001] The invention relates to the field of object classification, and relates to a method for improving the classification accuracy of vector graphics and bitmaps. Background technique [0002] With the rapid development of the mobile Internet, Internet entrepreneurship has become more and more popular, and many printing platforms have become more and more recognized by the public. Internet printing platforms have also emerged as the times require. However, with the addition of the Internet, many sensitive and illegal printing contents are transmitted to the production line of printed products through the Internet. , this kind of product has brought great harm to the society, so it is necessary to review the content before printing, but the amount of printed products every day is very large, and manual review is impractical, so intelligent computer review is needed. The current printed products are roughly divided into There are two types of classification ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 苏育挺王明兴张静
Owner TIANJIN UNIV
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