CN107358264A - A kind of method that graphical analysis is carried out based on machine learning algorithm - Google Patents
A kind of method that graphical analysis is carried out based on machine learning algorithm Download PDFInfo
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- CN107358264A CN107358264A CN201710576650.4A CN201710576650A CN107358264A CN 107358264 A CN107358264 A CN 107358264A CN 201710576650 A CN201710576650 A CN 201710576650A CN 107358264 A CN107358264 A CN 107358264A
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- 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
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The present invention proposes a kind of method that graphical analysis is carried out based on machine learning algorithm, and its main contents includes：Learn, by yardstick measure attribute, the consistent visualization of trunking mode including attribute, its process is to provide the attributive character of article in image to the deep learning of data set according to clasfficiator.Use supervised learning method, training machine learns to picture annotation, establish goods attribute grader, test performance standard is improved to greatest extent, machine is classified, it is applied to again bigger database, become the effective tool of picture analyzing, the present invention uses two novel visual algorithms, association attributes cluster is detected by image clustering, according to identified attribute cluster, further identify picture and article cluster, goods attribute is provided according to machine learning, for the new design of industrial circle, and the innovative solution of engineering field has done further contribution.
Description
Technical field
The present invention relates to art of image analysis, and graphical analysis is carried out based on machine learning algorithm more particularly, to a kind of
Method.
Background technology
Graphical analysis is usually used in the fields such as industry, detection, remote sensing, military affairs, typically using mathematical modeling and combines at image
The technology of reason analyzes low-level image feature and superstructure, has certain intelligent information so as to extract.Specifically, led in industry
Domain, be used for industrial automation, it is automatic to manipulate seam welder and cutting tool as mechanical hand captures object, for oil wells in field or
The mass data of seismic data is monitored and screened, and to automatic assembling and repairs offer visual feedback.In detection field, image
It is bad to analyze the wedge angle that can be checked on printed circuit board (PCB), short circuit and connection, examines the impurity in casting and crack, screens medical science
Image and faultage image, it is conventional to screen factory products.In remote sensing fields, cartography, traffic monitoring, resource pipe can apply to
Reason, mineral prospecting.In military field, available for pursuit movement object, self-navigation, target search and ranging etc..Although image
The research of analysis has been achieved with many achievements, and obtains practical application, but Unsupervised clustering on the specific object in many fields
Method also has certain limitation in granularity identification.
The present invention proposes a kind of method that graphical analysis is carried out based on machine learning algorithm, according to clasfficiator to data set
Deep learning provide the attributive character of article in image.Using supervised learning method, training machine learns to picture annotation,
Goods attribute grader is established, improves test performance standard to greatest extent, machine classified, then it is applied to bigger
Database, becomes the effective tool of picture analyzing, and the present invention uses two novel visual algorithms, examined by image clustering
Association attributes cluster is surveyed, according to identified attribute cluster, picture and article cluster is further identified, is provided according to machine learning
Goods attribute, further contribution is done for the new design of industrial circle, and the innovative solution of engineering field.
The content of the invention
For graphical analysis, two novel visual algorithms are proposed, association attributes cluster is detected by image clustering, according to
Identified attribute cluster, picture and article cluster are further identified, goods attribute is provided according to machine learning, is industrial circle
New design, and the innovative solution of engineering field done further contribution.
To solve the above problems, the present invention provides a kind of method that graphical analysis is carried out based on machine learning algorithm, it is led
Content is wanted to include：
(1) attribute learns；
(2) yardstick measure attribute is pressed；
(3) the consistent visualization of trunking mode.
Wherein, described attribute study, it is characterised in that supervised learning method, training machine learn to picture annotation,
The goods attribute inside new images is analyzed, wherein the key for creating convolutional neural networks (CNN) is network structure and interior striograph
Layer, CNN penults export a typical characteristic vector, and its dimension is 1024, wherein prediction scoring is exported by linear layer, such as institute
There is the probability of goods attribute, CNN learns image classification on the basis of based on ImageNet data sets, and data are carried out to CNN
On adjustment, according to Unsupervised clustering method, related data can be visualized with automatic detection, calculate the goods attribute of image.
Further, it is described to learn image classification on the basis of ImageNet data sets, it is characterised in that
After ImageNet study, last linear layer of network is not considered, it is 1024 that it, which exports dimension, parallel addition several 1024
×NiLinear layer, wherein i generic attributes number of labels are Ni, wherein running two novel visual algorithms on every image, one negative
Duty detection and positioning object, another is responsible for the observability for estimating image remainder, gives one group of images of items in the picture
And remaining visible part, the two is matched with the heuritic approach based on distance, according to the article position that detects and
Scale, a typical image is calculated, wherein the image beyond bounding box does not detect.
Further, described attribute tags, each attribute, an attribute tags are first selected using stratified sampling method, its
Secondary to specify a specific image for the attribute tags, wherein stratified sampling method counteracts the stealthy unbalance of each attribute, passes through
After 32 sampling, a small lot atlas is established, parameter gradients of the mini atlas relative to cross entropy error are calculated by CNN,
After each attribute runs up to certain gradient, gradient is made to decline momentum=0.9, learning rate=10-2, weight decay=10-4, with this
To update CNN parameter, wherein the attribute annotations for choosing image form new subset (SS), included largely wherein having annotated data set
Image, each image have 12 kinds of goods attributes, and its 80% SS is responsible for training, 10% be responsible for confirming time of deconditioning with
And this part is further analyzed, 10% is responsible for the attributive classification tested out and makes final evaluation.
Further, described SS, the Item Information extracted according to every pictures in data set, goods attribute phase
The annotation composition a subset of pass, with its attribute of machine learning and classification, its learning outcome is popularized and applied to whole data set,
Wherein goods attribute includes：Type of goods, purposes, color etc..
Further, it is described to press yardstick measure attribute, it is characterised in that to establish goods attribute grader, to greatest extent
Raising test performance standard, machine is classified, then it is applied to bigger database, and become picture searching has
Effect instrument, the results showed that, accuracy is corresponded to less than 50% according to goods attribute in image, grader predict the image be NO when
Time accounts for 99%, for its existing error, is scored using posterior probability model calibration grader, it is general that calibration method includes grader
Rate exports and isotonic regression.
Further, described grader probability output and isotonic regression, after isotonic regression algorithm is plus explaining, curve
Identification function be closely x=y, it means that curve meets standard, and wherein the test set of half is used for training regressor,
Other half is responsible for the reliability of calculated curve, and wherein isotonic regression usually requires substantial amounts of data to avoid overfitting, and
Based under probability output algorithm, wherein overfitting does not occur for test result, therefore calibrates grader to surveying using isotonic regression
Try the scoring of collection.
Further, the consistent visualization of described trunking mode, the visual theme repeated using clustering recognition, its
Middle visual theme is present in the embedded space of image, retrieves the layer second from the bottom of its network, and its feature space dimension is
1024, wherein can clearly be seen that its goods attribute is linear separation, in the feature space of dimension 1024, existed by machine
Training study in SS data sets, image is by article perceptual property classification display, wherein in 1024 embedded feature space of dimension
In, above-described multi view theme is detected by image clustering, this theme cluster is referred to as style cluster, according to
Identified style cluster, further identify picture and article cluster, the results showed that close relation between different clusters.
Further, described style cluster, a clustering algorithm is used in all data subsets, when reaching different
Between between image balance, wherein, by the partitioning of different regions different time, image is put into different receivers, wherein
Each receiver image is less than N, selects all pictures, makes each receiver image randomly select image N more than N, make N=
4000, the common 5.4M of sample drawn from cluster, according to this every image reduced in a series of, it is calculated in CNN
Dimension is 1024 characteristic vector, and wherein standard vector is L2, principal component analysis (PCA) is performed in standard vector, is being pushed up
In the composition project of layer, retain vectorial variance for 90%.
Further, described characteristic vector, according to the part and diagonal covariance square of 400 gauss hybrid models
Battle array, makes vectorial cluster, wherein according to maximum a posteriori probability, every image specifies an article cluster, afterwards according to cluster centers
Press instruction and calculate their Euclidean distance, will increase the possibility of data in model after cluster or make cluster scale
Reduce, therefore select 400 as large construction cluster and maximize the compromise of data possibility.
Brief description of the drawings
Fig. 1 is a kind of system flow chart for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.
Fig. 2 is a kind of framing detection figure for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.
Fig. 3 is a kind of data set figure for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.
Embodiment
It should be noted that in the case where not conflicting, the feature in embodiment and embodiment in the application can phase
Mutually combine, the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings.
Fig. 1 is a kind of system flow chart for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.Main bag
Include attribute study, by yardstick measure attribute, the consistent visualization of trunking mode.
Wherein, described attribute study, it is characterised in that supervised learning method, training machine learn to picture annotation,
The goods attribute inside new images is analyzed, wherein the key for creating convolutional neural networks (CNN) is network structure and interior striograph
Layer, CNN penults export a typical characteristic vector, and its dimension is 1024, wherein prediction scoring is exported by linear layer, such as institute
There is the probability of goods attribute, CNN learns image classification on the basis of based on ImageNet data sets, and data are carried out to CNN
On adjustment, according to Unsupervised clustering method, related data can be visualized with automatic detection, calculate the goods attribute of image.
Fig. 2 is a kind of framing detection figure for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.Institute
That states learns image classification on the basis of ImageNet data sets, after ImageNet study, does not consider the last of network
One linear layer, it is 1024 that it, which exports dimension, the parallel several 1024 × N of additioniLinear layer, wherein i generic attributes number of labels are
Ni, wherein running two novel visual algorithms on every image, one is responsible for detection and positioning object, and another is responsible for estimation
The observability of image remainder, one group of images of items and remaining visible part is given in the picture, with opening based on distance
Hairdo algorithm matches to the two, according to the article position and scale detected, a typical image is calculated, wherein exceeding
The image of bounding box does not detect.
Further, described attribute tags, each attribute, an attribute tags are first selected using stratified sampling method, its
Secondary to specify a specific image for the attribute tags, wherein stratified sampling method counteracts the stealthy unbalance of each attribute, passes through
After 32 sampling, a small lot atlas is established, parameter gradients of the mini atlas relative to cross entropy error are calculated by CNN,
After each attribute runs up to certain gradient, gradient is made to decline momentum=0.9, learning rate=10-2, weight decay=10-4, with this
To update CNN parameter, wherein the attribute annotations for choosing image form new subset (SS), included largely wherein having annotated data set
Image, each image have 12 kinds of goods attributes, and its 80% SS is responsible for training, 10% be responsible for confirming time of deconditioning with
And this part is further analyzed, 10% is responsible for the attributive classification tested out and makes final evaluation.
Fig. 3 is a kind of data set figure for the method that graphical analysis is carried out based on machine learning algorithm of the present invention.Described SS,
The Item Information extracted according to every pictures in data set, the related annotation of goods attribute is formed a subset, used
Its attribute of machine learning and classification, its learning outcome are popularized and applied to whole data set, and wherein goods attribute includes：Article kind
Class, purposes, color etc..
Further, it is described to press yardstick measure attribute, it is characterised in that to establish goods attribute grader, to greatest extent
Raising test performance standard, machine is classified, then it is applied to bigger database, and become picture searching has
Effect instrument, the results showed that, accuracy is corresponded to less than 50% according to goods attribute in image, grader predict the image be NO when
Time accounts for 99%, for its existing error, is scored using posterior probability model calibration grader, it is general that calibration method includes grader
Rate exports and isotonic regression.
Further, described grader probability output and isotonic regression, after isotonic regression algorithm is plus explaining, curve
Identification function be closely, it means that curve meets standard, and wherein the test set of half is used for training regressor, in addition
Half is responsible for the reliability of calculated curve, and wherein isotonic regression usually requires substantial amounts of data to avoid overfitting, and is based on
Under probability output algorithm, wherein overfitting does not occur for test result, therefore calibrates grader to test set using isotonic regression
Scoring.
Further, the consistent visualization of described trunking mode, the visual theme repeated using clustering recognition, its
Middle visual theme is present in the embedded space of image, retrieves the layer second from the bottom of its network, and its feature space dimension is
1024, wherein can clearly be seen that its goods attribute is linear separation, in the feature space of dimension 1024, existed by machine
Training study in SS data sets, image is by article perceptual property classification display, wherein in 1024 embedded feature space of dimension
In, above-described multi view theme is detected by image clustering, this theme cluster is referred to as style cluster, according to
Identified style cluster, further identify picture and article cluster, the results showed that close relation between different clusters.
Further, described style cluster, a clustering algorithm is used in all data subsets, when reaching different
Between between image balance, wherein, by the partitioning of different regions different time, image is put into different receivers, wherein
Each receiver image is less than N, selects all pictures, makes each receiver image randomly select image more than N, make N=
4000, the common 5.4M of sample drawn from cluster, according to this every image reduced in a series of, it is calculated in CNN
Dimension is 1024 characteristic vector, and wherein standard vector is principal component analysis (PCA) to be performed in standard vector, in top layer
In composition project, retain vectorial variance for 90%.
Further, described characteristic vector, according to the part and diagonal covariance square of 400 gauss hybrid models
Battle array, makes vectorial cluster, wherein according to maximum a posteriori probability, every image specifies an article cluster, afterwards according to cluster centers
Press instruction and calculate their Euclidean distance, will increase the possibility of data in model after cluster or make cluster scale
Reduce, therefore select 400 as large construction cluster and maximize the compromise of data possibility.
For those skilled in the art, the present invention is not restricted to the details of above-described embodiment, in the essence without departing substantially from the present invention
In the case of refreshing and scope, the present invention can be realized with other concrete forms.In addition, those skilled in the art can be to this hair
Bright to carry out various changes and modification without departing from the spirit and scope of the present invention, these improvement and modification also should be regarded as the present invention's
Protection domain.Therefore, appended claims are intended to be construed to include preferred embodiment and fall into all changes of the scope of the invention
More and change.
Claims (10)
- A kind of 1. method that graphical analysis is carried out based on machine learning algorithm, it is characterised in that mainly include attribute study (one)； By yardstick measure attribute (two)；The consistent visualization (three) of trunking mode.
- 2. (one) is learnt based on the attribute described in claims 1, it is characterised in that according to supervised learning method, training machine pair Picture annotation is learnt, and analyzes the goods attribute inside new images, wherein the key for creating convolutional neural networks (CNN) is net Network structure and interior image figure layer, CNN penults export a typical characteristic vector, and its dimension is 1024, wherein by linear layer Output prediction scoring, such as the probability of appearance goods attribute, CNN learns image point on the basis of based on ImageNet data sets Class, the adjustment in data is carried out to CNN, according to Unsupervised clustering method, related data can be visualized with automatic detection, calculate figure The goods attribute of picture.
- 3. based on learning image classification on the basis of ImageNet data sets described in claims 2, it is characterised in that After ImageNet study, last linear layer of network is not considered, it is 1024 that it, which exports dimension, parallel addition several 1024 ×NiLinear layer, wherein i generic attributes number of labels are Ni, wherein running two novel visual algorithms on every image, one negative Duty detection and positioning object, another is responsible for the observability for estimating image remainder, gives one group of images of items in the picture And remaining visible part, the two is matched with the heuritic approach based on distance, according to the article position that detects and Scale, a typical image is calculated, wherein the image beyond bounding box does not detect.
- 4. based on the attribute tags described in claims 2, it is characterised in that each attribute, first selected using stratified sampling method One attribute tags, secondly specifies a specific image for the attribute tags, and wherein stratified sampling method counteracts each attribute It is stealthy unbalance, by 32 times sampling after, establish a small lot atlas, mini atlas calculated relative to cross entropy by CNN The parameter gradients of error, after each attribute runs up to certain gradient, gradient is made to decline momentum=0.9, learning rate=10-2, weight Decay=10-4, CNN parameter is updated with this, wherein the attribute annotations for choosing image form new subset (SS), wherein having annotated Data set includes great amount of images, and each image has 12 kinds of goods attributes, and its 80% SS is responsible for training, and 10%, which is responsible for confirmation, stops Time for only training and this part is further analyzed, 10% is responsible for the attributive classification tested out and makes final evaluation.
- 5. based on the SS described in claims 4, it is characterised in that the article extracted according to every pictures in data set is believed Breath, the related annotation of goods attribute is formed a subset, with its attribute of machine learning and classification, its learning outcome promotes fortune Whole data set is used, wherein goods attribute includes：Type of goods, purposes, color etc..
- 6. based on pressing yardstick measure attribute (two) described in claims 1, it is characterised in that goods attribute grader is established, Test performance standard is improved to greatest extent, and machine is classified, then it is applied to bigger database, becomes picture The effective tool of analysis, the results showed that, accuracy is corresponded to less than 50% according to goods attribute in image, grader predicts the image To account for 99% when NO, for its existing error, scored using posterior probability model calibration grader, calibration method includes Grader probability output and isotonic regression.
- 7. based on the grader probability output described in claims 6 and isotonic regression, it is characterised in that isotonic regression algorithm adds After upper note, the identification function of curve is closely x=y, it means that curve meets the test set of standard, wherein half For training regressor, half is responsible for the reliability of calculated curve in addition, and wherein isotonic regression usually requires substantial amounts of data Avoid overfitting, and based under probability output algorithm, overfitting do not occur for wherein test result, therefore using isotonic regression come Calibrate scoring of the grader to test set.
- 8. the consistent visualization (three) based on the trunking mode described in claims 1, it is characterised in that using clustering recognition weight To appear again existing visual theme, wherein visual theme is present in the embedded space of image, retrieves the layer second from the bottom of its network, Its feature space dimension is 1024, wherein can clearly be seen that its goods attribute is linear separation, in the feature of dimension 1024 In space, learnt by training of the machine in SS data sets, image is by article perceptual property classification display, wherein in dimension In 1024 embedded feature spaces, above-described multi view theme is detected by image clustering, this theme cluster It is referred to as attribute cluster, according to identified attribute cluster, further identifies picture and article cluster, the results showed that different clusters Between close relation.
- 9. based on the style cluster described in claims 8, it is characterised in that with a cluster in all data subsets Algorithm, reach the balance of image between different time, wherein, by the partitioning of different regions different time, image is put into not Same receiver, wherein each receiver image is less than N, selects all pictures, makes each receiver image be taken out at random more than N Image N is taken, makes N=4000, the common 5.4M of sample drawn from cluster, according to this every image reduced in a series of, meter The characteristic vector that its dimension in CNN is 1024 is calculated, wherein standard vector is L2, principal component analysis is performed in standard vector (PCA), in the composition project of top layer, vectorial variance is retained for 90%.
- 10. based on the characteristic vector described in claims 9, it is characterised in that according to the composition portion of 400 gauss hybrid models Point and diagonal covariance matrix, make vectorial cluster, wherein according to maximum a posteriori probability, every image specifies an article cluster, Instruction is pressed according to cluster centers afterwards and calculate their Euclidean distance, will increase the possibility of data in model after cluster Property or make cluster scale reduce, therefore use 400 as large construction cluster and maximization data compromise selections.
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| US11416717B2 (en) | 2019-11-15 | 2022-08-16 | Institute For Information Industry | Classification model building apparatus and classification model building method thereof |
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| US11847661B2 (en) | 2017-11-30 | 2023-12-19 | 3M Innovative Properties Company | Image based counterfeit detection |
| CN111406270A (en) * | 2017-11-30 | 2020-07-10 | 3M创新有限公司 | Image-based counterfeit detection |
| CN108446330A (en) * | 2018-02-13 | 2018-08-24 | 北京数字新思科技有限公司 | Promotion object processing method and device and computer-readable storage medium |
| CN108446330B (en) * | 2018-02-13 | 2022-05-13 | 北京明略昭辉科技有限公司 | Promotion object processing method and device and computer-readable storage medium |
| CN108509567A (en) * | 2018-03-26 | 2018-09-07 | 湖北大学 | A kind of method and device that digital culture content library is built |
| CN112673369A (en) * | 2018-07-16 | 2021-04-16 | 微软技术许可有限责任公司 | Visual intent trigger for visual search |
| CN111344697A (en) * | 2018-07-18 | 2020-06-26 | 深圳码隆科技有限公司 | Complexity-based progressive training for machine vision models |
| CN109034147B (en) * | 2018-09-11 | 2020-08-11 | 上海唯识律简信息科技有限公司 | Optical character recognition optimization method and system based on deep learning and natural language |
| CN109034147A (en) * | 2018-09-11 | 2018-12-18 | 上海唯识律简信息科技有限公司 | Optical character identification optimization method and system based on deep learning and natural language |
| TWI762794B (en) * | 2019-01-28 | 2022-05-01 | 日商三菱電機股份有限公司 | Development support device, development support system, and development support method |
| CN113614773A (en) * | 2019-03-08 | 2021-11-05 | Sk电信有限公司 | Image analysis apparatus and method, and method for generating image analysis model for image analysis apparatus and method |
| CN110636020A (en) * | 2019-08-05 | 2019-12-31 | 北京大学 | Neural network equalization method for adaptive communication system |
| US11416717B2 (en) | 2019-11-15 | 2022-08-16 | Institute For Information Industry | Classification model building apparatus and classification model building method thereof |
| CN111429070A (en) * | 2020-04-02 | 2020-07-17 | 冯希 | Warehouse management system easy for classification recording |
| CN111882061A (en) * | 2020-07-24 | 2020-11-03 | 成都成信高科信息技术有限公司 | Convolutional neural network training method based on hierarchical random gradient descent |
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