http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105512682-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24147 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-758 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2015-12-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2018-11-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2018-11-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-105512682-B |
| titleOfInvention | A Classification Identification Method Based on Krawtchouk Moments and KNN-SMO Classifier |
| abstract | A classification identification method based on Krawtchouk moment and KNN‑SMO classifier, the identification method adopted is: apply the theory based on Krawtchouk moment and KNN‑SMO to the identification of classification identification in electronic forensics, the method firstly passes After the image preprocessing of the secret mark, the feature vector is formed by calculating the low-order Krawtchouk moment of the image, and then the KNN-SMO classifier is used to classify and recognize the secret mark image. On the one hand, the low-order Krawtchouk moment can be used to describe the characteristics of the image well, and the quantity has good stability under common attacks; The ability of SMO also has the advantage of overcoming the small sample problem, thereby improving the accuracy and speed of classified identification identification. |
| priorityDate | 2015-12-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID25572 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID415713197 |
Total number of triples: 13.