CN106446531B - A kind of pedigree tree constructing method based on priori decision model - Google Patents
A kind of pedigree tree constructing method based on priori decision model Download PDFInfo
- Publication number
- CN106446531B CN106446531B CN201610810480.7A CN201610810480A CN106446531B CN 106446531 B CN106446531 B CN 106446531B CN 201610810480 A CN201610810480 A CN 201610810480A CN 106446531 B CN106446531 B CN 106446531B
- Authority
- CN
- China
- Prior art keywords
- attribute
- species
- tree
- initial
- decision point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B10/00—ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Physiology (AREA)
- Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of pedigree tree constructing methods based on priori decision model, comprising the following steps: step 1, establishes initial genealogical tree using maximum parsimony method；Step 2, according to initial genealogical tree, priori decision model is established；Step 3, by priori decision model, judge the ownership of new species；Step 4, according to the ownership of new species, initial genealogical tree is updated.The present invention compares maximum parsimony method, and the higher species of missing ratio accuracy rate of position in genealogical tree greatly improves；The present invention avoids the influence to other species interspecies relations using species are grafted onto initial tree.
Description
Technical field
The invention belongs to bioinformatics technique fields, are related to a kind of method for constructing biological genealogical tree.
Background technique
At present when using biological morphology data building genealogical tree, the most commonly used is maximum parsimony method, but biomorph
Learning data especially paleobiomorphology data, inevitably there is a large amount of missing datas in acquisition, in missing data
Maximum parsimony method occur the problem that when ratio constantly rises
1. the appearance of the missing data in species will lead to its accuracy rate of position in genealogical tree and substantially reduce.
2. the confidence level of genealogical tree constructed by maximum parsimony method also can decrease, i.e., often due to there are missing data
Secondary obtained genealogical tree becomes extremely unstable.
3. the missing values in missing data species can have an impact the interspecies relation of species other in genealogical tree.
Summary of the invention
In view of the above-mentioned problems, the invention discloses a kind of pedigree tree constructing methods based on priori decision model.
A kind of pedigree tree constructing method based on priori decision model, comprising the following steps:
Step 1, initial genealogical tree is established using maximum parsimony method；
Step 2, according to initial genealogical tree, priori decision model is established using attribute reduction method；
Step 3, by priori decision model, judge the ownership of new species；
Step 4, according to the ownership of new species, initial genealogical tree is updated.
Further, initial genealogical tree is established using maximum parsimony method described in step 1 to refer to, will be free of missing data
Or the species containing a small amount of missing data are merged into a data set, to the data set with maximum parsimony method building initialization
Genealogical tree.
Further, according to initial genealogical tree described in step 2, priori decision model is established using attribute reduction method
Refer to:
Step 21, the node of optional one initial genealogical tree is present node, obtains the category of the subordinate species of the present node
Property set and tag along sort；
Step 22, it by the attribute set and tag along sort of the subordinate species of the present node, obtains deserving prosthomere
The subordinate species decision table of point；
Step 23, conditional attribute collection, building are obtained from the species without missing data or containing a small amount of missing data
The present node appurtenant kind attribute reduction set, obtains decision point corresponding with the present node；
Step 24, step 21-23 is repeated, until obtaining and the one-to-one decision point of all nodes of initial genealogical tree；
Step 25, the decision point according to step 24 constructs priori decision model.
Further, the present node appurtenant kind attribute reduction set of building described in step 23 refers to:
Step 231, present node subordinate species categorical attribute is calculated for the positive region of subordinate species attribute；
Step 232, the positive region of subordinate species attribute is calculated each by present node subordinate species categorical attribute
The different degree of present node subordinate species attribute；
Step 233, each section is chosen according to the sequence of the different degree of each present node subordinate species attribute from small to large
The attribute of point subordinate species as current attribute, a conditional attribute in optional conditions property set as conditions present attribute,
If after rejecting current attribute, the classification divided according to conditions present attribute is constant, then rejects the current attribute, it is current to obtain this
The attribute reduction set of conditional attribute collection；
It is concentrated from conditional attribute and rejects the conditions present attribute；
Step 234, step 233 is repeated, until conditional attribute integrates as empty set；
Further, by priori decision model described in step 3, judge that the ownership of new species refers to:
Step 41, the root decision point of priori decision model is chosen as current decision point；
Step 42, a set of properties is as current attribute group optionally in the current decision point, if new species and the current attribute
The attribute of group is identical, then new species belong to the current set of properties；
Otherwise, step 42 is repeated, until set of properties all in the current decision point is all different from new species attribute；
Step 43, if set of properties all in the current decision point is all different from new species attribute, priori decision is chosen
Next layer of decision point of model root decision point repeats step 42, until judging new species attribute.
Further, it updates initial genealogical tree according to the ownership of new species described in step 4 and refers to: according to new species
Attribute obtains the decision point that the new species are belonged to, and new species are grafted the initial pedigree tree node corresponding to its decision point
In, the initial genealogical tree that is updated.
Compared with prior art, the present invention has following technical effect that
1. the present invention compares maximum parsimony method, the higher species of missing ratio accuracy rate of position in genealogical tree mentions significantly
It is high；
2. the present invention avoids the influence to other species interspecies relations using species are grafted onto initial tree.
Detailed description of the invention
Fig. 1 is the foundation figure of initial genealogical tree branch node；
Fig. 2 is that initial genealogical tree individual node establishes schematic diagram；
Fig. 3 is decision point illustraton of model；
Fig. 4 is initial genealogical tree priori decision model figure；
Fig. 5 is that initial pedigree tree node establishes branch (grafting) schematic diagram；
Fig. 6 is the family tree diagram handled without missing；
Fig. 7 is family tree diagram constructed by the present invention；
Fig. 8 is the family tree diagram of maximum parsimony method building；
Fig. 9 is that Palaearctic parasite species of Testudinidae missing data ratio and species are flat
Equal accuracy rate experimental result picture.
Specific embodiment
Below in conjunction with drawings and examples, the present invention is further illustrated.
A kind of pedigree tree constructing method based on priori decision model, comprising the following steps:
Step 1, initial genealogical tree is established using maximum parsimony method；
Species without missing data or containing a small amount of missing data are put forward to be merged into a data set, with most
Big parsimony principle constructs the higher genealogical tree of confidence level, or according to the existing priori knowledge building genealogical tree of researcher oneself, and with
This is as initial tree.Node is established to the place for occurring branch in initial tree, as shown in Figure 1.The end of genealogical tree is to participate in structure
The species of genealogical tree are built, the species set for being subordinated to present node, the biological species at each node are suffered from each node
Naturally it is divided into different classifications, and has obvious tag along sort.
In Fig. 1, circle indicates that node, box indicate species.Branch node is established respectively according to 4 branches in figure, is marked
Number it is followed successively by N1、N2、N3、N4, each branch node suffers from the species for being subordinated to the node.In N1At node, subordinate species are
A, B, C, D, E can be divided into subordinate species two classes by the branch of node, and A is one kind, and B, C, D, E are one kind.And N2Under node
Subordinate species be B, C, D, E, then subordinate species are divided into B, C and D, two class of E.In N3And N4Subordinate species are distinguished at node
For B, C and D, E.Two nodes are also divided into two classes, N3Node is that B is a kind of and C is a kind of, N4Node is that D one kind and E are a kind of.
Step 2, according to initial genealogical tree, priori decision model is established；
Wherein, described according to initial genealogical tree, it establishes priori decision model and refers to:
Step 21, the node of optional one initial genealogical tree is present node, obtains the category of the subordinate species of the present node
Property set and tag along sort；
Step 22, it by the attribute set and tag along sort of the subordinate species of the present node, obtains deserving prosthomere
The subordinate species decision table of point；
Step 23, conditional attribute collection, building are obtained from the species without missing data or containing a small amount of missing data
The present node appurtenant kind attribute reduction set, obtains decision point corresponding with the present node；
Step 24, step 21-23 is repeated, until obtaining and the one-to-one decision point of all nodes of initial genealogical tree；
Step 25, the decision point according to step 24 constructs priori decision model.
Wherein, the present node appurtenant kind attribute reduction set of building described in step 23 refers to:
Step 231, present node subordinate species categorical attribute is calculated for the positive region of subordinate species attribute；
Step 232, the positive region of subordinate species attribute is calculated each by present node subordinate species categorical attribute
The different degree of present node subordinate species attribute；
Step 233, each section is chosen according to the sequence of the different degree of each present node subordinate species attribute from small to large
The attribute of point subordinate species as current attribute, a conditional attribute in optional conditions property set as conditions present attribute,
If after rejecting current attribute, the classification divided according to conditions present attribute is constant, then rejects the current attribute, it is current to obtain this
The attribute reduction set of conditional attribute collection；
It is concentrated from conditional attribute and rejects the conditions present attribute；
Step 234, step 233 is repeated, until conditional attribute integrates as empty set；
It is combined using Attribute Reduction Set obtained above and replaces set of properties corresponding to the present node to get current to this
The corresponding decision point of node.
The present embodiment is by taking a node in initial genealogical tree as an example, to illustrate the establishment process of decision point.Such as Fig. 2 institute
Show, N is the node in initial genealogical tree, and the subordinate species of N node are respectively present in A tree and B-tree.It can establish currently with this
The decision point of node.
Regard node subordinate species attribute set and tag along sort as decision table, the knowledge representation system of decision point subordinate species
System may be defined as S=(U, R, V, f), wherein U be domain, R be decision point subordinate species attribute collection, R=C ∪ D and C ∩ D=Φ,
C is known as decision point subordinate species morphological properties collection, and D is known as decision point subordinate species tag attributes collection, and f:U × R → V is one
A information function, it specifies the attribute value of each object X in U.
Define 1: in information system S, for attribute setIndiscernible relation is defined as:
Obvious IND (P) is also equivalence relation, and U/R indicates the institute of R
There is equivalence class.
2: given information system decision-making table S=(U, R, V, f) is defined, for each subsetAnd Indiscernible relation
The lower aprons collection and upper approximate set of P, X can be respectively defined as:
The present embodiment carries out reduction to entire conditional attribute collection C, using the heuristic information of positive region gradually by the set
In unnecessary attribute divide out, but still meet the classification of current decision point, the attribute set Reduct guaranteed be one about
Letter, i.e. attribute decision group Reduct1, the attribute for participating in establishing set of properties in original attribute set is rejected, to remaining condition category
Property collection continues attribute reduction and obtains corresponding Reducti, until conditional attribute collection cannot construct attribute set again.
As follows is the specific implementation process of the present embodiment:
Input: decision point subordinate species information system decision table
S=(U, R, V, f), R=C ∪ D are decision point subordinate species attribute set, C={ ai, i=1,2 ... ..., m } claim
For conditional attribute collection, decision point subordinate species decision kind set is D={ di, i=1,2 ... ..., n }；
Step 1 calculates decision point subordinate species categorical attribute D for the positive region POS of subordinate species attribute Cc(D)；
Step 2, to each present node subordinate species attribute aiIt calculates
Step 3, enables Reduct=C；By attribute aiBy the sequence arrangement of pos from small to large, behaviour is executed to each attribute
Make；IfThen attribute aiAnswer reduction, Reduct=Reduct- { ai}；Otherwise aiIt cannot be by about
Letter, Reduct are constant；
Step 4 obtains attribute reduction group Reducti, the attribute for participating in establishing set of properties is rejected, i.e. residue
=C-Reducti；
Step 5 assigns C, i.e. C=residue residue condition property set residue；
Step 6 executes Step 1, establishes more set of properties reduct of decision pointi(i=1,2 ... n) until C condition category
Until property collection can not construct set of properties；
Output: decision point set of properties set reducti(i=1,2 ... n)；
By the building to Fig. 2 interior joint subordinate species attribute reduction set, decision corresponding with node is obtained
Point finally constructs decision point model, as shown in figure 3, reduct1To reductnFor the set of properties set in decision point.
Corresponding decision point is established to each node in initial genealogical tree, and then obtains the decision priori mould of initial genealogical tree
Type, the tree topology of model and the structure of initial tree are consistent, as shown in figure 4, the circle in figure indicates decision point, box is indicated
Species.
Initial genealogical tree shares 4 nodes in Fig. 1, and the decision point of correspondence establishment is the P in Fig. 41、P2、P3、P4.Decision model
The end of type structure is species, corresponding with species in Fig. 1, is A, B, C, D, E.
Step 3, by priori decision model, judge the ownership of new species；
Wherein, specific steps are as follows:
Step 31, the root decision point of priori decision model is chosen as current decision point；
Step 32, a set of properties is as current attribute group optionally in the current decision point, if new species and the current attribute
The attribute of group is identical, then new species belong to the current set of properties；
Otherwise, step 32 is repeated, until set of properties all in the current decision point is all different from new species attribute；
Step 33, if set of properties all in the current decision point is all different from new species attribute, priori decision is chosen
Next layer of decision point of model root decision point repeats step 32, until judging new species attribute.
The present embodiment sets the set of properties number for belonging to A tree as m, and the set of properties number for belonging to B-tree is n.By determining to new species
The attribute of each set of properties in plan point compares, and same alike result group such as occurs, then determines new species in the attribute of current decision point
Group ownership, that is, belong to A tree or belong to B-tree, and adds up to ownership set of properties number.If being both not belonging to A tree or being not belonging to B
Attribute belonging to missing data is the attribute currently judged in decision point in set of properties in tree or new species, then can not judge
The ownership of new species is not counted in the set of properties number of any subtree ownership, i.e. m, n are without cumulative.Complete returning for each set of properties
After belonging to judgement, set of properties the number m, n of new species subtree ownership are finally obtained.Then new species decision point belongs to determination strategy are as follows:
According to above-mentioned decision point determination strategy, new species judge ownership since the root decision point of priori decision model, recognize
Surely after belonging to A subtree or B subtree, continue ownership judgement for the root decision point of ownership subtree.This behaviour is executed repeatedly
Make until stopping judging.
Step 4, according to the ownership of new species, initial genealogical tree is updated.
After new species stop judgement, according to new species attribute, the decision point that the new species are belonged to is obtained, new species are transferred
It connects in the initial pedigree tree node corresponding to its decision point, the initial genealogical tree updated.New species graft subtree process
As shown in figure 5, judging ownership course in the stopping of P decision point, and graft the node N in the corresponding initial genealogical tree of P decision point
On, become the new branch of initial genealogical tree.
In order to verify the validity of this method, experiment is with without missing data or having containing a small amount of missing data
The biological morphology data set of pedigree is specified as experimental data set, missing at random data processing, fortune are carried out to species therein
Experimental comparison is carried out with method proposed by the present invention and maximum parsimony method.
It is tested with Palaearctic parasite species of Testudinidae data set, to species
T.marocana carries out missing at random processing and obtains respectively with the data without missing processing with maximum parsimony method and this method
To genealogical tree result be compared.Such as Fig. 6, shown in 7,8.
Each species in data set are subjected to missing at random processing, utilize the method and maximum parsimony method structure proposed in text
The genealogical tree built carries out accuracy rate comparison.In Fig. 9, abscissa indicates the missing data ratio of species, from 0% to 70%.It is vertical
The Average Accuracy of coordinate representation species.
Claims (4)
1. a kind of pedigree tree constructing method based on priori decision model, comprising the following steps:
Step 1, initial genealogical tree is established using maximum parsimony method；
It is characterized in that, further comprising the steps of:
Step 2, according to initial genealogical tree, priori decision model is established using attribute reduction method, is specifically comprised the following steps:
Step 21, the node of optional one initial genealogical tree is present node, obtains the property set of the subordinate species of the present node
Conjunction and tag along sort；
Step 22, by the attribute set and tag along sort of the subordinate species of the present node, the present node is obtained
Subordinate species decision table；
Step 23, conditional attribute collection is obtained from the species without missing data or containing a small amount of missing data, building is deserved
Front nodal point subordinate species attribute reduction set, obtains decision point corresponding with the present node；
Step 24, step 21-23 is repeated, until obtaining and the one-to-one decision point of all nodes of initial genealogical tree；
Step 25, the decision point according to step 24 constructs priori decision model；
Step 3, by priori decision model, judge the ownership of new species, specifically comprise the following steps:
Step 41, the root decision point of priori decision model is chosen as current decision point；
Step 42, a set of properties is as current attribute group optionally in the current decision point, if new species and the current set of properties
Attribute is identical, then new species belong to the current set of properties；
Otherwise, step 42 is repeated, until set of properties all in the current decision point is all different from new species attribute；
Step 43, if set of properties all in the current decision point is all different from new species attribute, priori decision model is chosen
Next layer of decision point of root decision point repeats step 42, until judging new species attribute；
Step 4, according to the ownership of new species, initial genealogical tree is updated.
2. pedigree tree constructing method as described in claim 1, which is characterized in that use maximum parsimony method described in step 1
It establishes initial genealogical tree to refer to, the species without missing data or containing a small amount of missing data is merged into a data set,
To the data set with maximum parsimony method building initialization genealogical tree.
3. pedigree tree constructing method as described in claim 1, which is characterized in that the present node of building described in step 23
Subordinate species attribute reduction set refers to:
Step 231, present node subordinate species categorical attribute is calculated for the positive region of subordinate species attribute；
Step 232, the positive region of subordinate species attribute is calculated each current by present node subordinate species categorical attribute
The different degree of node subordinate species attribute；
Step 233, according to the different degree of each present node subordinate species attribute sequence from small to large choose each node from
The attribute of species is as current attribute, and a conditional attribute in optional conditions property set is as conditions present attribute, if picking
After current attribute, the classification divided according to conditions present attribute is constant, then rejects the current attribute, obtain the conditions present
The attribute reduction set of property set；
It is concentrated from conditional attribute and rejects the conditions present attribute；
Step 234, step 233 is repeated, until conditional attribute integrates as empty set.
4. pedigree tree constructing method as described in claim 1, which is characterized in that returning according to new species described in step 4
Belong to, updates initial genealogical tree and refer to: according to new species attribute, obtaining the decision point that the new species are belonged to, new species are grafted
In the initial pedigree tree node corresponding to its decision point, the initial genealogical tree that is updated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610810480.7A CN106446531B (en) | 2016-09-08 | 2016-09-08 | A kind of pedigree tree constructing method based on priori decision model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610810480.7A CN106446531B (en) | 2016-09-08 | 2016-09-08 | A kind of pedigree tree constructing method based on priori decision model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106446531A CN106446531A (en) | 2017-02-22 |
| CN106446531B true CN106446531B (en) | 2019-03-22 |
Family
ID=58165275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610810480.7A Active CN106446531B (en) | 2016-09-08 | 2016-09-08 | A kind of pedigree tree constructing method based on priori decision model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106446531B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509764B (en) * | 2018-02-27 | 2020-06-16 | 西北大学 | Ancient organism pedigree evolution analysis method based on genetic attribute reduction |
| CN109326328B (en) * | 2018-11-02 | 2021-08-03 | 西北大学 | Pedigree clustering-based ancient organism pedigree evolution analysis method |
| CN112817959B (en) * | 2021-02-25 | 2023-03-24 | 西北大学 | Construction method of ancient biomorphic phylogenetic tree based on multi-metric index weight |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101082925A (en) * | 2007-07-09 | 2007-12-05 | 山西大学 | Rough set property reduction method based on SQL language |
| CN103093118A (en) * | 2013-02-07 | 2013-05-08 | 中国科学院计算机网络信息中心 | Rebuilding method of phylogenetic tree |
-
2016
- 2016-09-08 CN CN201610810480.7A patent/CN106446531B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101082925A (en) * | 2007-07-09 | 2007-12-05 | 山西大学 | Rough set property reduction method based on SQL language |
| CN103093118A (en) * | 2013-02-07 | 2013-05-08 | 中国科学院计算机网络信息中心 | Rebuilding method of phylogenetic tree |
Non-Patent Citations (4)
| Title |
|---|
| Missing data and the design of phylogenetic analyses;John J. Wiens;《Journal of Biomedical Informatics》;20061231;第39卷;第34-42页 |
| Recursive algorithms for phylogenetic tree counting;Alexandra Gavryushkina et al.;《 Algorithms for Molecular Biology》;20131231;第8卷(第26期);第1-13页 |
| 粗糙集理论中属性相对约简算法;张腾飞 等;《电子学报》;20051130;第33卷(第11期);第2080-2083页 |
| 系统发育分析中的最大简约法及其优化;郑巍 等;《昆虫学报》;20131031;第56卷(第10期);第1217-1228页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106446531A (en) | 2017-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103812872B (en) | A kind of network navy behavioral value method and system based on mixing Di Li Cray process | |
| CN106446531B (en) | A kind of pedigree tree constructing method based on priori decision model | |
| CN100595780C (en) | Handwriting digital automatic identification method based on module neural network SN9701 rectangular array | |
| CN106372648A (en) | Multi-feature-fusion-convolutional-neural-network-based plankton image classification method | |
| CN106096661B (en) | The zero sample image classification method based on relative priority random forest | |
| CN104281674B (en) | It is a kind of based on the adaptive clustering scheme and system that gather coefficient | |
| CN103544506A (en) | Method and device for classifying images on basis of convolutional neural network | |
| CN109753995A (en) | A kind of network optimization structure divided based on 3D target classification and Scene Semantics | |
| CN109685110A (en) | Training method, image classification method and device, the server of image classification network | |
| CN101937436B (en) | Text classification method and device | |
| CN105046287B (en) | A kind of online more strokes repeat the cluster and approximating method of skeletonizing | |
| CN107392314A (en) | A kind of deep layer convolutional neural networks method that connection is abandoned based on certainty | |
| CN101980211A (en) | Machine learning model and establishing method thereof | |
| CN101980210A (en) | Marked word classifying and grading method and system | |
| CN105956184A (en) | Method for identifying collaborative and organized junk information release team in micro-blog social network | |
| CN107506350A (en) | A kind of method and apparatus of identification information | |
| CN107145516A (en) | A kind of Text Clustering Method and system | |
| CN109670037A (en) | K-means Text Clustering Method based on topic model and rough set | |
| CN104217015A (en) | Hierarchical clustering method based on mutual shared nearest neighbors | |
| CN110532399A (en) | Knowledge mapping update method, system and the device of object game question answering system | |
| CN104331523A (en) | Conceptual object model-based question searching method | |
| CN107945199A (en) | Infrared Image Segmentation and system based on bat algorithm and Otsu algorithm | |
| CN104732524A (en) | Random weight network partitioning method for blood leukocyte microscopic image | |
| CN107301426A (en) | A kind of multi-tag clustering method of shoe sole print image | |
| CN112860996B (en) | Interest point processing method and device, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20191018 Address after: 710065 room 20305, 3 / F, unit 2, building 2, leading Times Plaza (block B), No. 86, Gaoxin Road, hi tech Zone, Xi'an City, Shaanxi Province Patentee after: Shaanxi nugget Data Technology Co. Ltd. Address before: 710069 Shaanxi city of Xi'an province Taibai Road No. 229 Patentee before: Northwest University |