CN105931253B - A kind of image partition method being combined based on semi-supervised learning - Google Patents
A kind of image partition method being combined based on semi-supervised learning Download PDFInfo
- Publication number
- CN105931253B CN105931253B CN201610321142.7A CN201610321142A CN105931253B CN 105931253 B CN105931253 B CN 105931253B CN 201610321142 A CN201610321142 A CN 201610321142A CN 105931253 B CN105931253 B CN 105931253B
- Authority
- CN
- China
- Prior art keywords
- image
- gained
- carried out
- node
- split
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005192 partition Methods 0.000 title claims abstract description 10
- 239000011159 matrix material Substances 0.000 claims abstract description 22
- 238000006243 chemical reaction Methods 0.000 claims abstract description 20
- 238000000605 extraction Methods 0.000 claims abstract description 11
- 239000003550 marker Substances 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims abstract description 10
- 230000011218 segmentation Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000000007 visual effect Effects 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 6
- 230000006835 compression Effects 0.000 claims description 3
- 238000007906 compression Methods 0.000 claims description 3
- 238000005530 etching Methods 0.000 claims description 3
- 238000012804 iterative process Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 abstract description 10
- 230000008901 benefit Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20036—Morphological image processing
- G06T2207/20041—Distance transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20152—Watershed segmentation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of image partition methods being combined based on semi-supervised learning, include the following steps：Obtain the gradient image of image to be split, and treat segmentation image compressed, sharpened successively, binary conversion treatment and range conversion processing, obtain image to be split apart from topographic map；The gray value of each connected region is more maximum in extraction gained range conversion figure or point set, as foreground label；Watershed transform is carried out apart from topographic map to gained, using obtained watershed crestal line as context marker；The local minimum in the gradient image is shielded, the local minimum of the gradient image is marked according to the foreground of acquisition label and context marker, obtains revised gradient image；Then the acquisition of data of multiple angles, the foundation of prediction matrix, the structure of training pattern and the segmentation of image are carried out by semi-supervised learning method.The present invention can improve the precision of image segmentation.
Description
Technical field
The present invention relates to a kind of pattern dividing methods, and in particular to a kind of image segmentation being combined based on semi-supervised learning
Method.
Background technology
Image segmentation, as an important branch in image procossing and computer vision field, is inhaled always with Objective extraction
Draw the concern of numerous researchers.Meanwhile image segmentation and Objective extraction are in pattern-recognition, computer vision, artificial intelligence etc.
Field also has a wide range of applications.Therefore, to the further investigation of image segmentation and Objective extraction not only facilitate image segmentation with
The perfect solution of Objective extraction, and help to push the development in the fields such as pattern-recognition, computer vision, artificial intelligence.
Currently, image segmentation is mainly used to realize the classification of the data of unknown classification, in Analysis of Medical Treatment Data, credit card
Credit is classified and there is great meaning in the fields such as image classification, once studying successfully and putting into application, will generate huge society
Meeting and economic benefit.But the data (such as image in internet) in real world are no class label mostly, and sample
This artificial calibration process is very time-consuming and laborious and expensive so that the Accurate classification of data acquires a certain degree of difficulty.Recently, it is based on similar
The semi-supervised learning method of figure construction has been risen in the related fields such as data mining and pattern classification as powerful and popular
Effective tool.Based on the data characteristics in real world, semi-supervised learning mainly by there is the classification of calibration sample, and has label
Similitude between unlabeled exemplars discloses the classification of unlabeled exemplars
Traditional image partition method has：Mean shift process normalizes dividing method and K Mean Methods etc., generally deposits
It is low in segmentation precision, target of the adhesion region without apparent boundary can not be divided, and be easy mutually to obscure with image background to cause to owe
Segmentation.
Invention content
To solve the above problems, the present invention provides a kind of image partition method being combined based on semi-supervised learning, carry
The high precision of image separation.
To achieve the above object, the technical solution that the present invention takes is：
A kind of image partition method being combined based on semi-supervised learning, is included the following steps：
The noise level of S1, extraction image to be split, and adjust according to gained noise level the bit of the image to be split
Rate and resolution ratio, and with the bit rate and resolution compression of the gained image to be split；
S2, gray-scale map is generated according to the pixel edge strength of image obtained by step S1, and based on the gray-scale map to institute
The Edge contrast of image is obtained, and obtains the gradient image of the image of gained；
S3, binary conversion treatment and range conversion are carried out successively to the image for sharpening gained, obtains the distance of image to be split
Transformation Graphs, and complete the conversion of range conversion figure, obtain image to be split apart from topographic map；
The gray value of each connected region is more maximum in S4, extraction gained range conversion figure or point set, as foreground
Label；Watershed transform is carried out apart from topographic map to gained, using obtained watershed crestal line as context marker；
Local minimum in S5, the shielding gradient image, according to gained foreground label and context marker label
The local minimum of gradient image obtains revised gradient image.
S6, revised gradient image is divided into a certain number of region units, each image-region after segmentation is regarded
For a node, three images of each region unit are established to the mapping of graphics, and are counted the syntople between vertex, calculated
Connect the weight on the side on two vertex；
S7, all nodes are divided into mark node and do not mark node, wherein mark node occupies the minority；
S8, acquisition include the different visual angles sample data by various visual angles character representation for marking node and not marking node；
S9, the different visual angles sample data of gained is subjected to similarity-based learning, constructs similar neighborhoods figure, weight is calculated
Coefficient matrix, and symmetrization, normalized are carried out to the weight coefficient matrix；
S10, it is marked according to the foreground, initializes a class label matrix；
S11, non-negative sparse is carried out based on the weight coefficient matrix after the class label matrix and symmetrization, normalized
The iterative process that label is propagated, obtains prediction matrix；
S12, the similarity probabilities characterized according to gained prediction matrix, prediction do not mark node image different visual angles sample number
According to accurate classification, obtain direct-push image classification as a result, training complete semisupervised classification modeling, generate training pattern；
S13, classification letter is carried out to the node image pattern to be sorted that do not mark in test set using gained training pattern
The prediction of breath obtains the class label for not marking node image pattern to be sorted in test set, to realize to image data
Cutting procedure.
Wherein, in the gray-scale map each pixel gray scale be video image in corresponding pixel points edge strength.
Wherein, the Edge contrast includes：
Expansive working and/or Gaussian Blur operation are carried out to the gray-scale map, obtain intermediate image A；
Etching operation is executed to the intermediate image A, obtains intermediate image A1；
Processing is sharpened to video image based on the intermediate image A1.
The invention has the advantages that：
Processing is sharpened to image by selectivity, is avoided doing over-sharpening；It is pre- to first pass through what range conversion obtained
Foreground marks the context marker obtained with watershed transform to correct the gradient image of image to be split, then uses semi-supervised segmentation
Method is split the revised gradient image, obtains image segmentation result, is carried out to gradient image so both having remained
Watershed transform, can effective position target object edge, the advantages of being partitioned into target object integrity profile, and foreground mark can be passed through
Note and context marker distinguish target of the adhesion region without apparent boundary so that the phenomenon that being not in less divided and over-segmentation, can
To be suitable for the image segmentation field of object edge adhesion well, the precision of image segmentation is improved, promotion pattern is contributed to
The development in the fields such as identification, computer vision, artificial intelligence.
Specific implementation mode
In order to make objects and advantages of the present invention be more clearly understood, the present invention is carried out with reference to embodiments further
It is described in detail.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to limit this hair
It is bright.
An embodiment of the present invention provides a kind of image partition methods being combined based on semi-supervised learning, including walk as follows
Suddenly：
The noise level of S1, extraction image to be split, and adjust according to gained noise level the bit of the image to be split
Rate and resolution ratio, and with the bit rate and resolution compression of the gained image to be split；
S2, gray-scale map is generated according to the pixel edge strength of image obtained by step S1, and based on the gray-scale map to institute
The Edge contrast of image is obtained, and obtains the gradient image of the image of gained；
S3, binary conversion treatment and range conversion are carried out successively to the image for sharpening gained, obtains the distance of image to be split
Transformation Graphs, and complete the conversion of range conversion figure, obtain image to be split apart from topographic map；
The gray value of each connected region is more maximum in S4, extraction gained range conversion figure or point set, as foreground
Label；Watershed transform is carried out apart from topographic map to gained, using obtained watershed crestal line as context marker；
Local minimum in S5, the shielding gradient image, according to gained foreground label and context marker label
The local minimum of gradient image obtains revised gradient image.
S6, revised gradient image is divided into a certain number of region units, each image-region after segmentation is regarded
For a node, three images of each region unit are established to the mapping of graphics, and are counted the syntople between vertex, calculated
Connect the weight on the side on two vertex；
S7, all nodes are divided into mark node and do not mark node, wherein mark node occupies the minority；
S8, acquisition include the different visual angles sample data by various visual angles character representation for marking node and not marking node；
S9, the different visual angles sample data of gained is subjected to similarity-based learning, constructs similar neighborhoods figure, weight is calculated
Coefficient matrix, and symmetrization, normalized are carried out to the weight coefficient matrix；
S10, it is marked according to the foreground, initializes a class label matrix；
S11, non-negative sparse is carried out based on the weight coefficient matrix after the class label matrix and symmetrization, normalized
The iterative process that label is propagated, obtains prediction matrix；
S12, the similarity probabilities characterized according to gained prediction matrix, prediction do not mark node image different visual angles sample number
According to accurate classification, obtain direct-push image classification as a result, training complete semisupervised classification modeling, generate training pattern；
S13, classification letter is carried out to the node image pattern to be sorted that do not mark in test set using gained training pattern
The prediction of breath obtains the class label for not marking node image pattern to be sorted in test set, to realize to image data
Cutting procedure.
Wherein, in the gray-scale map each pixel gray scale be video image in corresponding pixel points edge strength.
Wherein, the Edge contrast includes：
Expansive working and/or Gaussian Blur operation are carried out to the gray-scale map, obtain intermediate image A；
Etching operation is executed to the intermediate image A, obtains intermediate image A1；
Processing is sharpened to video image based on the intermediate image A1.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, it can also make several improvements and retouch, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of image partition method being combined based on semi-supervised learning, which is characterized in that include the following steps：
The noise level of S1, extraction image to be split, and according to gained noise level adjust the image to be split bit rate and
Resolution ratio, and with the bit rate and resolution compression of the gained image to be split；
S2, gray-scale map is generated according to the pixel edge strength of image obtained by step S1, and based on the gray-scale map to gained figure
The Edge contrast of picture, and obtain the gradient image of the image of gained；
S3, binary conversion treatment and range conversion are carried out successively to the image for sharpening gained, obtains the range conversion of image to be split
Figure, and completes the conversion of range conversion figure, obtain image to be split apart from topographic map；
The gray value of each connected region is more maximum in S4, extraction gained range conversion figure or point set, as foreground label；
Watershed transform is carried out apart from topographic map to gained, using obtained watershed crestal line as context marker；
Local minimum in S5, the shielding gradient image marks the gradient according to gained foreground label and context marker
The local minimum of image obtains revised gradient image；
S6, revised gradient image is divided into a certain number of region units, each image-region after segmentation is considered as one
A node establishes three images of each region unit to the mapping of graphics, and counts the syntople between vertex, calculates connection
The weight on the side on two vertex；
S7, all nodes are divided into mark node and do not mark node, wherein mark node occupies the minority；
S8, acquisition include the different visual angles sample data by various visual angles character representation for marking node and not marking node；
S9, the different visual angles sample data of gained is subjected to similarity-based learning, constructs similar neighborhoods figure, weight coefficient is calculated
Matrix, and symmetrization, normalized are carried out to the weight coefficient matrix；
S10, it is marked according to the foreground, initializes a class label matrix；
S11, non-negative sparse label is carried out based on the weight coefficient matrix after the class label matrix and symmetrization, normalized
The iterative process of propagation, obtains prediction matrix；
S12, the similarity probabilities characterized according to gained prediction matrix, prediction do not mark node image different visual angles sample data
Accurate classification obtains direct-push image classification as a result, semisupervised classification modeling, generation training pattern are completed in training；
S13, classification information is carried out to the node image pattern to be sorted that do not mark in test set using gained training pattern
Prediction, obtains the class label for not marking node image pattern to be sorted in test set, is divided image data with realizing
Cut process.
2. a kind of image partition method being combined based on semi-supervised learning according to claim 1, which is characterized in that institute
The gray scale for stating each pixel in gray-scale map is the edge strength of corresponding pixel points in video image.
3. a kind of image partition method being combined based on semi-supervised learning according to claim 1, the Edge contrast
Including：
Expansive working and/or Gaussian Blur operation are carried out to the gray-scale map, obtain intermediate image A；
Etching operation is executed to the intermediate image A, obtains intermediate image A1；
Processing is sharpened to video image based on the intermediate image A1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610321142.7A CN105931253B (en) | 2016-05-16 | 2016-05-16 | A kind of image partition method being combined based on semi-supervised learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610321142.7A CN105931253B (en) | 2016-05-16 | 2016-05-16 | A kind of image partition method being combined based on semi-supervised learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105931253A CN105931253A (en) | 2016-09-07 |
| CN105931253B true CN105931253B (en) | 2018-11-06 |
Family
ID=56835930
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610321142.7A Expired - Fee Related CN105931253B (en) | 2016-05-16 | 2016-05-16 | A kind of image partition method being combined based on semi-supervised learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105931253B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107527349A (en) * | 2017-06-27 | 2017-12-29 | 广州城建职业学院 | Image partition method based on network dynamics evolutionary strategy |
| CN107808162A (en) * | 2017-09-27 | 2018-03-16 | 广东电网有限责任公司江门供电局 | A kind of transformer substation protection pressure plate state comparison method and device |
| CN107967688B (en) * | 2017-12-21 | 2021-06-15 | 联想(北京)有限公司 | Method and system for segmenting object in image |
| CN108629777A (en) * | 2018-04-19 | 2018-10-09 | 麦克奥迪（厦门）医疗诊断系统有限公司 | A kind of number pathology full slice image lesion region automatic division method |
| CN108898618B (en) * | 2018-06-06 | 2021-09-24 | 上海交通大学 | Weak surveillance video object segmentation method and device |
| CN109523563A (en) * | 2018-11-01 | 2019-03-26 | 华东师范大学 | A method of improving micro-image annotating efficiency |
| CN109460735B (en) * | 2018-11-09 | 2021-02-02 | 中国科学院自动化研究所 | Document binarization processing method, system and device based on graph semi-supervised learning |
| CN109829472B (en) * | 2018-12-24 | 2024-05-14 | 陕西师范大学 | Semi-supervised classification method based on probability nearest neighbor |
| CN109740658B (en) * | 2018-12-28 | 2023-04-18 | 陕西师范大学 | Semi-supervised image classification method based on weighted graph |
| CN110517217B (en) * | 2019-01-23 | 2020-04-28 | 莱芜职业技术学院 | Computer integral data recognition device |
| CN111204046B (en) * | 2019-04-09 | 2021-12-07 | 苏州爱柯基科技有限公司 | Saving type box body 3D printing system and method |
| CN109834941B (en) * | 2019-04-09 | 2020-08-28 | 台州明创科技有限公司 | Saving type box body 3D printing system |
| CN110119965A (en) * | 2019-05-17 | 2019-08-13 | 浙江和也健康科技有限公司 | A kind of mattress information collector, mattress after-sale service system and method |
| CN111767920B (en) * | 2020-06-30 | 2023-07-28 | 北京百度网讯科技有限公司 | Method and device for extracting region of interest, electronic equipment and storage medium |
| CN112348826B (en) * | 2020-10-26 | 2023-04-07 | 陕西科技大学 | Interactive liver segmentation method based on geodesic distance and V-net |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102013017A (en) * | 2010-11-26 | 2011-04-13 | 华中科技大学 | Method for roughly sorting high-resolution remote sensing image scene |
| CN102902978A (en) * | 2012-08-31 | 2013-01-30 | 电子科技大学 | Object-oriented high-resolution remote-sensing image classification method |
| CN104408731A (en) * | 2014-12-09 | 2015-03-11 | 西安电子科技大学 | Region graph and statistic similarity coding-based SAR (synthetic aperture radar) image segmentation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9129382B2 (en) * | 2010-06-25 | 2015-09-08 | Institute Of Automation, Chinese Academy Of Sciences | Method and system for brain tumor segmentation in multi-parameter 3D MR images via robust statistic information propagation |
-
2016
- 2016-05-16 CN CN201610321142.7A patent/CN105931253B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102013017A (en) * | 2010-11-26 | 2011-04-13 | 华中科技大学 | Method for roughly sorting high-resolution remote sensing image scene |
| CN102902978A (en) * | 2012-08-31 | 2013-01-30 | 电子科技大学 | Object-oriented high-resolution remote-sensing image classification method |
| CN104408731A (en) * | 2014-12-09 | 2015-03-11 | 西安电子科技大学 | Region graph and statistic similarity coding-based SAR (synthetic aperture radar) image segmentation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105931253A (en) | 2016-09-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105931253B (en) | A kind of image partition method being combined based on semi-supervised learning | |
| CN105608690B (en) | A kind of image partition method being combined based on graph theory and semi-supervised learning | |
| CN105389550B (en) | It is a kind of based on sparse guide and the remote sensing target detection method that significantly drives | |
| CN104239902B (en) | Hyperspectral image classification method based on non local similitude and sparse coding | |
| CN105069481B (en) | Natural scene multiple labeling sorting technique based on spatial pyramid sparse coding | |
| CN105956560A (en) | Vehicle model identification method based on pooling multi-scale depth convolution characteristics | |
| CN109766835A (en) | The SAR target identification method of confrontation network is generated based on multi-parameters optimization | |
| CN111340034B (en) | Text detection and identification method and system for natural scene | |
| CN107944483B (en) | Multispectral image classification method based on dual-channel DCGAN and feature fusion | |
| CN105469047A (en) | Chinese detection method based on unsupervised learning and deep learning network and system thereof | |
| CN103903013A (en) | Optimization algorithm of unmarked flat object recognition | |
| CN103886342A (en) | Hyperspectral image classification method based on spectrums and neighbourhood information dictionary learning | |
| CN105335975B (en) | Polarization SAR image segmentation method based on low-rank decomposition and statistics with histogram | |
| CN103745233B (en) | The hyperspectral image classification method migrated based on spatial information | |
| CN108021869A (en) | A kind of convolutional neural networks tracking of combination gaussian kernel function | |
| CN109255339B (en) | Classification method based on self-adaptive deep forest human gait energy map | |
| CN112905828B (en) | Image retriever, database and retrieval method combining significant features | |
| CN102968796A (en) | SAR (Synthetic Aperture Radar) image segmentation method based on sampling learning | |
| CN105117740A (en) | Font identification method and device | |
| CN102436645B (en) | Spectral clustering image segmentation method based on MOD dictionary learning sampling | |
| CN111598155A (en) | Fine-grained image weak supervision target positioning method based on deep learning | |
| CN105894035B (en) | SAR image classification method based on SAR-SIFT and DBN | |
| CN106778897A (en) | Plant species recognition methods twice based on COS distance and center profile distance | |
| Basri et al. | Recognition of Indonesian Sign Language Alphabets Using Fourier Descriptor Method | |
| CN106548195A (en) | A kind of object detection method based on modified model HOG ULBP feature operators |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181106 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |