A Classification Identification Method Based on Krawtchouk Moment and KNN-SMO Classifier

Abstract

A secret-level identification method based on Krawtchouk moment and KNN-SMO classifier, the identification method adopted is: applying the theory based on Krawtchouk moment and KNN-SMO to the identification of secret-level identification in electronic forensics, the method first passes After image preprocessing of the dense-level identification, the feature vector is formed by calculating the low-order Krawtchouk moments of the image, and then the KNN-SMO classifier is used to classify and identify the dense-level identification image. On the one hand, the low-order Krawtchouk moment can be used to describe the features of the image well, and the quantity has good stability under common attacks. On the other hand, the KNN-SMO combined classifier makes the classifier both have KNN fast classification It also has the advantages of SMO in overcoming the problem of small samples, thereby improving the accuracy and speed of the identification of classified labels.

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 (Aravinda C V, Prakash HN. Template matching method for Kannada Handwritten recognition based on correlation analysis [C] / / Contemporary Computing and Informatics (IC3I), 2014International Conference on. IEEE, 2014: 857-861.), Character Feature Statistics (Das S, jyotiChoudhury S, Das A K, et al. Selection of Graph-Based Features for Character Recognition Using Similarity Based Feature Dependency ...

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 , and the quantity has good stability under common attacks, and the use of KNN-SMO combined classifier makes the classifier not only have the ability of KNN fast classification but also has the advantage of SMO in overcoming the small sample problem, thus improving the confidentiality identification Recognition accuracy and speed

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