CN107909097B - Method and device for updating samples in sample library - Google Patents

Method and device for updating samples in sample library Download PDF

Info

Publication number
CN107909097B
CN107909097B CN201711093134.2A CN201711093134A CN107909097B CN 107909097 B CN107909097 B CN 107909097B CN 201711093134 A CN201711093134 A CN 201711093134A CN 107909097 B CN107909097 B CN 107909097B
Authority
CN
China
Prior art keywords
sample
preset
determining
threshold
accuracy
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
Application number
CN201711093134.2A
Other languages
Chinese (zh)
Other versions
CN107909097A (en
Inventor
周书恒
祝慧佳
赵智源
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
Original Assignee
Advanced New Technologies Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Advanced New Technologies Co Ltd filed Critical Advanced New Technologies Co Ltd
Priority to CN201711093134.2A priority Critical patent/CN107909097B/en
Publication of CN107909097A publication Critical patent/CN107909097A/en
Application granted granted Critical
Publication of CN107909097B publication Critical patent/CN107909097B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The embodiment of the specification provides a method and a device for updating samples in a sample library. And calculating the similarity value of the sample and a plurality of preset samples added with sample labels. And determining a prediction label corresponding to each threshold according to the similarity value and the plurality of thresholds. And for the prediction label corresponding to each threshold, determining the corresponding accuracy and recall ratio according to the prediction label and the sample label, thereby determining a plurality of accuracies and recall ratios. And calculating the average accuracy AP of the sample according to the determined plurality of accuracies and recall rates. And if the AP meets the preset condition, updating the sample into a sample library.

Description

Method and device for updating samples in sample library
Technical Field
One or more embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method and an apparatus for updating samples in a sample library.
Background
At present, similarity search has been widely applied to many fields such as information retrieval, multimedia, and machine vision. The effect of similarity search is mainly based on the quality of the samples in the sample library, and in the traditional technology, the quality control of the samples in the sample library mainly depends on manual evaluation. Therefore, there is a need to provide a more reliable solution for controlling the quality of samples in a sample library.
Disclosure of Invention
One or more embodiments of the present disclosure describe a method and an apparatus for updating samples in a sample library, which can more reliably control the quality of the samples in the sample library.
In a first aspect, a method for updating samples in a sample library is provided, including:
acquiring a first sample to be updated to a sample library;
calculating similarity values of the first sample and a plurality of preset samples added with sample labels;
determining a prediction label corresponding to each threshold value according to the similarity value and a plurality of threshold values;
for the prediction label corresponding to each threshold, determining corresponding accuracy and recall rate according to the prediction label and the sample label, thereby determining a plurality of accuracy rates and recall rates;
calculating the average accuracy AP of the first sample according to the determined multiple accuracies and recall rates;
and if the AP meets a preset condition, updating the first sample into the sample library.
In a second aspect, an apparatus for updating samples in a sample library is provided, including:
the device comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a first sample to be updated to a sample library;
a calculating unit, configured to calculate similarity values between the first sample acquired by the acquiring unit and a plurality of preset samples to which sample labels have been added;
a determining unit, configured to determine, according to the similarity value calculated by the calculating unit and the plurality of threshold values, a predicted label corresponding to each threshold value;
the determining unit is further configured to determine, for the prediction tag corresponding to each threshold, a corresponding accuracy and a recall ratio according to the prediction tag and the sample tag, so as to determine a plurality of accuracies and recall ratios;
the calculating unit is further configured to calculate an average accuracy AP of the first sample according to the plurality of accuracies and recall rates determined by the determining unit;
and the updating unit is used for updating the first sample into the sample library when the AP calculated by the calculating unit meets a preset condition.
The method and the device for updating the samples in the sample library, provided by one or more embodiments of the present specification, obtain the samples to be updated to the sample library. And calculating the similarity value of the sample and a plurality of preset samples added with sample labels. And determining a prediction label corresponding to each threshold according to the similarity value and the plurality of thresholds. And for the prediction label corresponding to each threshold, determining the corresponding accuracy and recall ratio according to the prediction label and the sample label, thereby determining a plurality of accuracies and recall ratios. And calculating the average accuracy AP of the sample according to the determined plurality of accuracies and recall rates. And if the AP meets the preset condition, updating the sample into a sample library. Thereby, the quality of the samples in the sample database can be controlled more reliably.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic application scenario diagram of a method for updating samples in a sample library according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a method for updating samples in a sample library according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram illustrating a method for updating samples in a sample library according to another embodiment of the present disclosure;
fig. 4 is a schematic diagram of an apparatus for updating samples in a sample library according to an embodiment of the present disclosure.
Detailed Description
The scheme provided by the specification is described below with reference to the accompanying drawings.
The method for updating the samples in the sample library provided by one embodiment of the present specification may be applied to the scenario shown in fig. 1, and in fig. 1, the samples in the sample library may be collected by a human from a background database of a server in advance. The samples in the sample library may have corresponding sample labels (e.g., 1 and 0). Specifically, if the sample label of the sample is: 1, the sample is a positive sample; and if the sample label of the sample is: and "0", the sample is a negative sample.
In fig. 1, the similarity search system may search for a sample that is most similar or most dissimilar to the current sample from the sample library.
Of course, in practical applications, the method for updating the samples in the sample library provided in the embodiment of the present specification may also be applied to other scenarios. This is not a limitation of the present specification.
Fig. 2 is a flowchart of a method for updating samples in a sample library according to an embodiment of the present disclosure. The execution subject of the method may be a device with processing capabilities: a server or a system or device. As shown in fig. 2, the method may specifically include:
step 210, a first sample to be updated to a sample library is obtained.
The first sample here may be any sample to be updated that is to be updated to the sample repository.
Step 220, calculating similarity values between the first sample and a plurality of preset samples with sample labels added.
In one implementation, the calculating of the similarity value between the first sample and the plurality of preset samples to which the sample labels are added in step 220 may be implemented by:
and step A, determining sample characteristic vectors of the first sample, and determining the sample characteristic vectors of all preset samples.
With the first sample: for example, B can be expressed as: b ═ B1,b2,...,bn) Then b1,b2,...,bnNamely a first sample: b sample feature vectors. The above n is used to indicate the number of sample feature vectors of sample B. Furthermore, it may also be assumed that there are m preset samples: x1,X2,...,XmThe m preset samples may be expressed as:
Figure BDA0001461686600000041
wherein x is1,1,x1,2,...,x1,nFor the preset samples: x1N isSample feature vector, x2,1,x2,2,...,x2,nFor the preset samples: x2N sample feature vectors, and so on.
And B, calculating the distance value between the first sample and each preset sample according to the sample characteristic vector of the first sample and the sample characteristic vector of each preset sample.
With the ith preset sample (X)i) For example, the first sample: b and XiThe distance value of (d) can be expressed as the following equation:
di＝||Xi-B||2i ═ 1,2,3,. ·, m (equation 1)
Wherein d isiIs the distance value between the first sample and the ith preset sample, and m is the number of the preset samples.
And step C, normalizing the distance value.
And D, determining a similarity value according to the distance value after the normalization processing.
For example, the normalization process is performed on the ith distance value, and the formula of the normalization process can be as follows:
simi＝1-di100, i ═ 1,2,.. m (equation 2)
Wherein, simiIs the similarity value between the first sample and the ith preset sample, diThe distance value between the first sample and the ith preset sample is obtained.
Of course, in practical applications, the similarity value between the first sample and each preset sample may also be calculated by other manners, for example, different values of the first sample and the preset sample may be calculated. And then determining a similarity value according to the different values, etc., which is not described herein again.
And step 230, determining a prediction label corresponding to each threshold according to the similarity value and the plurality of thresholds.
The multiple thresholds may be set empirically, and in different application scenarios, the multiple thresholds may have different values.
In addition, the prediction tag may be defined as the sample tag, and may include: "0" and "1", which are not repeated herein.
In one implementation, the similarity value of each preset sample may be compared with a threshold, and if the similarity value of a certain preset sample exceeds the threshold, the prediction label of the preset sample may be determined as: a "1" (or "0"), otherwise the prediction tag of the preset sample may be determined as: "0" (or "1"). It can be understood that, according to the above-mentioned manner for determining the prediction tag of one preset sample, the prediction tag of each preset sample can be determined.
The setting mode of the prediction flag of the preset sample (set to "1" or "0") may be determined according to the setting mode of the sample flag.
And 240, determining corresponding accuracy and recall rate according to the prediction label and the sample label for the prediction label corresponding to each threshold value, thereby determining a plurality of accuracy rates and recall rates.
For example, the accuracy and the recall rate corresponding to one threshold are determined, the number of the preset samples (marked as TP) with the prediction label of "1" and the sample label of "1" can be counted according to the sample label and the prediction label of each preset sample; and counting the number of the preset samples with the prediction label of '1' and the sample label of '0' (marked as: FP) and the number of the preset samples with the prediction label of '0' and the sample label of '1' (marked as: FN). The following can be done according to the formula: TP/(TP + FP) to determine the accuracy rate according to the formula: TP/(TP + FN) to determine recall.
It should be noted that, when the thresholds are different, the prediction labels of the preset samples are different, so that the statistical TP, FP and FN are inconsistent, and thus the determined accuracy and recall rate are inconsistent. Therefore, the accuracy and recall in this specification correspond to the threshold values.
And (c) repeatedly executing the steps a to c until the accuracy and the recall ratio corresponding to each threshold value are determined.
And step 250, calculating the average accuracy AP of the first sample according to the plurality of determined accuracies and recall rates.
In one implementation, after determining the accuracy and Recall corresponding to the plurality of thresholds, a Precision-Recall (P-R) curve is plotted in a planar rectangular coordinate system with the Recall corresponding to each threshold as an x-coordinate and the accuracy as a y-coordinate. And determining the area of the accuracy-recall rate curve enclosed by the first quadrant of the plane rectangular coordinate system and the x axis and the y axis. From this area, the Average Accuracy (AP) is determined.
Of course, in practical applications, the AP may also be determined in other manners, for example, taking an average value of all accuracy rates, which is not described herein again.
And step 260, updating the first sample to a sample library when the AP meets a preset condition.
In one implementation, a threshold value may be preset, which may be set based on empirical values. When the AP exceeds the threshold, the first sample is updated to the sample base. Thus, a more refined evaluation of the sample can be performed.
In other implementation manners, the first threshold with the corresponding accuracy as the preset value may be further selected from the plurality of thresholds. In one example, the preset value may be 90%. That is, a threshold with an accuracy of 90% is selected from the plurality of thresholds. And then updating the corresponding relation of the first sample, the first threshold and the AP into a sample library. For example, the contents of the sample library may be as shown in table 1.
TABLE 1
Sample(s) First threshold value AP
Sample
1 0.8 0.9
Sample 2 0.6 0.9
... ... ...
The first threshold in table 1 may be used as a similarity threshold of the sample, that is, a similar sample or a different sample of the sample may be searched according to the first threshold. Taking the search for similar samples as an example, the specific process may be: a matching value of the sample to the candidate sample may be calculated. If the matching degree value exceeds the first threshold corresponding to the sample, the candidate sample can be selected as a similar sample of the sample. Taking the first threshold corresponding to 90% as an example, the principle of selecting the candidate sample with the matching degree value exceeding the first threshold as the similar sample of the sample is as follows: when the matching value of a candidate sample to the sample is greater than a first threshold, there is a 90% confidence that the candidate sample may fall into the sample library of the sample.
It should be noted that, after the similar sample to the first sample is selected, the similar sample may also be updated to the sample library.
It should be further noted that table 1 is only an exemplary illustration given for the convenience of understanding the present embodiment, and is not a limitation of the present embodiment. Other content, such as sample labels, etc., may also be included in table 1.
In summary, the method provided by the above embodiments of the present specification can more accurately and quickly implement the cleaning and updating of the sample library. In addition, the method is automatically completed, so that the application requirement of big data can be met. Furthermore, since the threshold values of the samples are updated in the sample library at the same time, a basis can be provided for recalling and comprehensively scoring the samples of specific types.
Fig. 3 is a schematic diagram illustrating an updating method of samples in a sample library according to another embodiment of the present disclosure, and in fig. 3, a distance between a sample to be updated and a preset sample may be calculated based on a sample feature vector, and the normalized distance may be used as a similarity value. According to the similarity value and the sample label of the preset sample (expressed as Y ═ Y1,y2,...,ym),yiE {0,1}, i 1, 2.. m) and a plurality of thresholds to generate an accuracy-recall curve. Then, a threshold (sim) with an accuracy of 0.9 can be selectedt) And calculating the area enclosed by the accuracy-recall ratio curve and the x axis, and taking the area as the AP value. Finally, if the AP value exceeds the threshold, the sample, s imtAnd the AP updates to the sample repository. Therefore, the quality and generalization capability of the samples in the sample library can be improved more effectively and rapidly.
Corresponding to the method for updating samples in a sample library, an embodiment of the present disclosure further provides an apparatus for updating samples in a sample library, as shown in fig. 4, the apparatus includes:
an obtaining unit 401 is configured to obtain a first sample to be updated to a sample library.
A calculating unit 402, configured to calculate similarity values between the first sample acquired by the acquiring unit 401 and a plurality of preset samples to which sample labels have been added.
Optionally, the computing unit 402 may specifically be configured to:
and determining a sample feature vector of the first sample, and determining a sample feature vector of each preset sample.
And calculating the distance value between the first sample and each preset sample according to the sample characteristic vector of the first sample and the sample characteristic vector of each preset sample.
And normalizing the distance value.
And determining a similarity value according to the distance value after the normalization processing.
A determining unit 403, configured to determine, according to the similarity value calculated by the calculating unit 402 and the plurality of threshold values, a predicted label corresponding to each threshold value.
The determining unit 403 is further configured to determine, for the prediction label corresponding to each threshold, a corresponding accuracy and a recall ratio according to the prediction label and the sample label, so as to determine a plurality of accuracies and recall ratios.
The calculating unit 402 is further configured to calculate an average accuracy AP of the first sample according to the plurality of accuracies and recalls determined by the determining unit 403.
Optionally, the computing unit 402 may specifically be configured to:
and drawing an accuracy-recall ratio curve in a plane rectangular coordinate system by taking the recall ratio corresponding to each threshold as an x coordinate and the accuracy as a y coordinate.
And determining the area of the accuracy-recall ratio curve enclosed by the first quadrant of the plane rectangular coordinate system and the x axis and the y axis.
From the area, the AP is determined.
An updating unit 404, configured to update the first sample to the sample library when the AP calculated by the calculating unit 402 satisfies a preset condition.
Optionally, the updating unit 404 may specifically be configured to:
and selecting a first threshold with the corresponding accuracy as a preset value from the plurality of thresholds.
And updating the corresponding relation of the first sample, the first threshold and the AP into a sample library.
Optionally, the apparatus may further include: a selection unit 405.
The calculating unit 402 is further configured to calculate a matching value of the first sample and the candidate sample.
A selecting unit 405, configured to select the candidate sample as a similar sample of the first sample if the matching degree value calculated by the calculating unit 402 exceeds a first threshold.
Optionally, the updating unit 404 is further configured to update the similar sample of the first sample into the sample library.
The functions of each functional module of the device in the above embodiments of the present description may be implemented through each step of the above method embodiments, and therefore, a specific working process of the device provided in one embodiment of the present description is not repeated herein.
In the apparatus for updating samples in a sample library provided in one embodiment of the present specification, the obtaining unit 401 obtains a first sample to be updated to the sample library. The calculating unit 402 calculates similarity values between the first sample and a plurality of preset samples to which sample labels have been added. The determination unit 403 determines a predicted label corresponding to each threshold value from the similarity value and the plurality of threshold values. For the prediction label corresponding to each threshold, the determining unit 403 determines the corresponding accuracy and recall rate according to the prediction label and the sample label, thereby determining a plurality of accuracy rates and recall rates. The calculating unit 402 calculates an average accuracy AP of the first sample according to the plurality of accuracies and the recall rate. When the AP satisfies a preset condition, the updating unit 404 updates the first sample to the sample library. Thereby, the quality of the samples in the sample database can be controlled more reliably.
Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.

Claims (12)

1. A method for updating samples in a sample library, comprising:
acquiring a first sample to be updated to a sample library;
calculating similarity values of the first sample and a plurality of preset samples added with sample labels;
determining a prediction label of each preset sample corresponding to each threshold value in the plurality of threshold values according to the similarity value and the plurality of threshold values;
determining the accuracy and the recall rate corresponding to each threshold value according to the prediction label and the sample label of each preset sample corresponding to each threshold value, thereby determining a plurality of accuracy rates and recall rates corresponding to the plurality of threshold values;
calculating the average accuracy AP of the first sample according to the determined multiple accuracies and recall rates;
and if the AP meets a preset condition, updating the first sample into the sample library.
2. The method of claim 1, wherein calculating the similarity value between the first sample and a plurality of preset samples with sample labels added comprises:
determining a sample characteristic vector of the first sample, and determining a sample characteristic vector of each preset sample;
calculating the distance value between the first sample and each preset sample according to the sample characteristic vector of the first sample and the sample characteristic vector of each preset sample;
normalizing the distance value;
and determining the similarity value according to the distance value after the normalization processing.
3. The method according to claim 1 or 2, wherein said calculating an average accuracy rate AP of said first samples based on said determined plurality of accuracy rates and recall rates comprises:
drawing an accuracy-recall rate curve in a plane rectangular coordinate system by taking the recall rate corresponding to each threshold as an x coordinate and the accuracy as a y coordinate;
determining the area of the accuracy-recall curve enclosed by the first quadrant of the plane rectangular coordinate system and the x axis and the y axis;
and determining the AP according to the area.
4. The method of claim 1, wherein the updating the first sample into the sample library comprises:
selecting a first threshold with a corresponding accuracy as a preset value from the plurality of thresholds;
updating the first sample, the first threshold, and the correspondence of the APs into the sample library.
5. The method of claim 4, further comprising:
calculating a matching degree value of the first sample and a candidate sample;
if the matching degree value exceeds the first threshold value, the candidate sample is selected as a similar sample of the first sample.
6. The method of claim 5, further comprising:
updating similar samples of the first sample into the sample library.
7. An apparatus for updating samples in a sample library, comprising:
the device comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a first sample to be updated to a sample library;
a calculating unit, configured to calculate similarity values between the first sample acquired by the acquiring unit and a plurality of preset samples to which sample labels have been added;
a determining unit, configured to determine, according to the similarity value calculated by the calculating unit and a plurality of thresholds, a prediction label of each preset sample corresponding to each of the plurality of thresholds;
the determining unit is further configured to determine, for the prediction tag of each preset sample corresponding to each threshold, an accuracy and a recall corresponding to each threshold according to the prediction tag and the sample tag, so as to determine a plurality of accuracies and recalls corresponding to the plurality of thresholds;
the calculating unit is further configured to calculate an average accuracy AP of the first sample according to the plurality of accuracies and recall rates determined by the determining unit;
and the updating unit is used for updating the first sample into the sample library when the AP calculated by the calculating unit meets a preset condition.
8. The apparatus according to claim 7, wherein the computing unit is specifically configured to:
determining a sample characteristic vector of the first sample, and determining a sample characteristic vector of each preset sample;
calculating the distance value between the first sample and each preset sample according to the sample characteristic vector of the first sample and the sample characteristic vector of each preset sample;
normalizing the distance value;
and determining the similarity value according to the distance value after the normalization processing.
9. The apparatus according to claim 7 or 8, wherein the computing unit is specifically configured to:
drawing an accuracy-recall rate curve in a plane rectangular coordinate system by taking the recall rate corresponding to each threshold as an x coordinate and the accuracy as a y coordinate;
determining the area of the accuracy-recall curve enclosed by the first quadrant of the plane rectangular coordinate system and the x axis and the y axis;
and determining the AP according to the area.
10. The apparatus according to claim 7, wherein the updating unit is specifically configured to:
selecting a first threshold with a corresponding accuracy as a preset value from the plurality of thresholds;
updating the first sample, the first threshold, and the correspondence of the APs into the sample library.
11. The apparatus of claim 10, further comprising: selecting a unit;
the calculating unit is further used for calculating a matching degree value of the first sample and a candidate sample;
the selecting unit is configured to select the candidate sample as a similar sample of the first sample if the matching degree value calculated by the calculating unit exceeds the first threshold.
12. The apparatus of claim 11,
the updating unit is further configured to update the similar samples of the first sample into the sample library.
CN201711093134.2A 2017-11-08 2017-11-08 Method and device for updating samples in sample library Active CN107909097B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711093134.2A CN107909097B (en) 2017-11-08 2017-11-08 Method and device for updating samples in sample library

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711093134.2A CN107909097B (en) 2017-11-08 2017-11-08 Method and device for updating samples in sample library

Publications (2)

Publication Number Publication Date
CN107909097A CN107909097A (en) 2018-04-13
CN107909097B true CN107909097B (en) 2021-07-30

Family

ID=61842856

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711093134.2A Active CN107909097B (en) 2017-11-08 2017-11-08 Method and device for updating samples in sample library

Country Status (1)

Country Link
CN (1) CN107909097B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110348993B (en) * 2019-06-28 2023-12-22 北京淇瑀信息科技有限公司 Determination method and determination device for label for wind assessment model and electronic equipment
CN114219026A (en) * 2021-12-15 2022-03-22 中兴通讯股份有限公司 Data processing method and device and computer readable storage medium
CN117634489A (en) * 2023-11-29 2024-03-01 中国电子投资控股有限公司 Sample selection method, sample selection system, identification method and identification system
CN117976063A (en) * 2024-03-28 2024-05-03 青岛大数华创科技有限公司 Quality approval management system for biological resource library

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103208008A (en) * 2013-03-21 2013-07-17 北京工业大学 Fast adaptation method for traffic video monitoring target detection based on machine vision
CN104516899A (en) * 2013-09-29 2015-04-15 北大方正集团有限公司 Updating method and device for word stock
CN105118518A (en) * 2015-07-15 2015-12-02 百度在线网络技术（北京）有限公司 Sound semantic analysis method and device
CN105320778A (en) * 2015-11-25 2016-02-10 焦点科技股份有限公司 Commodity labeling method suitable for electronic commerce Chinese website
CN105913051A (en) * 2016-05-26 2016-08-31 京东方科技集团股份有限公司 Device and method for updating template library for face image recognition
CN106503045A (en) * 2016-09-21 2017-03-15 联动优势科技有限公司 A kind of method and device for updating ATL

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156885B (en) * 2010-02-12 2014-03-26 中国科学院自动化研究所 Image classification method based on cascaded codebook generation
US9330167B1 (en) * 2013-05-13 2016-05-03 Groupon, Inc. Method, apparatus, and computer program product for classification and tagging of textual data
CN104127179B (en) * 2014-04-13 2016-04-06 北京工业大学 The brain electrical feature extracting method of a kind of advantage combination of electrodes and empirical mode decomposition
US20170032247A1 (en) * 2015-07-31 2017-02-02 Qualcomm Incorporated Media classification

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103208008A (en) * 2013-03-21 2013-07-17 北京工业大学 Fast adaptation method for traffic video monitoring target detection based on machine vision
CN104516899A (en) * 2013-09-29 2015-04-15 北大方正集团有限公司 Updating method and device for word stock
CN105118518A (en) * 2015-07-15 2015-12-02 百度在线网络技术（北京）有限公司 Sound semantic analysis method and device
CN105320778A (en) * 2015-11-25 2016-02-10 焦点科技股份有限公司 Commodity labeling method suitable for electronic commerce Chinese website
CN105913051A (en) * 2016-05-26 2016-08-31 京东方科技集团股份有限公司 Device and method for updating template library for face image recognition
CN106503045A (en) * 2016-09-21 2017-03-15 联动优势科技有限公司 A kind of method and device for updating ATL

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
Cost-Sensitive Multi-Label Learning for Audio Tag Annotation and Retrieval;Hung-Yi Lo et al;《IEEE Transactions on Multimedia》;20110630;第13卷(第3期);第518-529页 *
Integrating Distance Metric Learning Into Label Propagation Model For Multi-Label Image Annotation;Bin Wang et al;《18th IEEE International Conference on Image Processing》;20111231;第3649-3652页 *
SVM词库智能更新技术在搜索分类中的应用;齐富民等;《计算机工程与设计》;20140630;第35卷(第6期);第2017-2023页 *
线上线下结合的实时手势跟踪系统;王超等;《桂林电子科技大学学报》;20120430;第32卷(第2期);第125-128页 *

Also Published As

Publication number Publication date
CN107909097A (en) 2018-04-13

Similar Documents

Publication Publication Date Title
CN107909097B (en) Method and device for updating samples in sample library
CN108280477B (en) Method and apparatus for clustering images
CN113379718B (en) Target detection method, target detection device, electronic equipment and readable storage medium
CN108830329B (en) Picture processing method and device
CN109829435B (en) Video image processing method, device and computer readable medium
CN108363729B (en) Character string comparison method and device, terminal equipment and storage medium
CN106971401B (en) Multi-target tracking device and method
US11954148B2 (en) Matching audio fingerprints
CN112847343A (en) Dynamic target tracking and positioning method, device, equipment and storage medium
CN109766436A (en) A kind of matched method and apparatus of data element of the field and knowledge base of tables of data
CN110598559B (en) Method and device for detecting motion direction, computer equipment and storage medium
US20150142812A1 (en) Methods And Systems For Query Segmentation In A Search
CN107291745B (en) Data index management method and device
CN111078639A (en) Data standardization method and device and electronic equipment
WO2020119136A1 (en) Position determination
CN109165572B (en) Method and apparatus for generating information
CN113239149B (en) Entity processing method, device, electronic equipment and storage medium
CN116383423A (en) Image retrieval result rearrangement method, device, computer equipment and medium
CN111275693B (en) Counting method and counting device for objects in image and readable storage medium
CN110598769B (en) User group discovery method, device, equipment and computer readable storage medium
US20210318422A1 (en) Method and apparatus for detecting signal propagation type
US11790635B2 (en) Learning device, search device, learning method, search method, learning program, and search program
US11558715B2 (en) Method and apparatus for detecting signal propagation type
CN111104922A (en) Feature matching algorithm based on ordered sampling
CN114972807B (en) Method and device for determining image recognition accuracy, electronic equipment and medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20201019

Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands

Applicant after: Innovative advanced technology Co.,Ltd.

Address before: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands

Applicant before: Advanced innovation technology Co.,Ltd.

Effective date of registration: 20201019

Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands

Applicant after: Advanced innovation technology Co.,Ltd.

Address before: A four-storey 847 mailbox in Grand Cayman Capital Building, British Cayman Islands

Applicant before: Alibaba Group Holding Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant