CN110059595A - A kind of adaptive algorithm for image recognition - Google Patents
A kind of adaptive algorithm for image recognition Download PDFInfo
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- CN110059595A CN110059595A CN201910264244.3A CN201910264244A CN110059595A CN 110059595 A CN110059595 A CN 110059595A CN 201910264244 A CN201910264244 A CN 201910264244A CN 110059595 A CN110059595 A CN 110059595A
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- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 43
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 230000002068 genetic effect Effects 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 238000007405 data analysis Methods 0.000 claims abstract description 7
- 238000000513 principal component analysis Methods 0.000 claims abstract description 5
- 238000005452 bending Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 230000006978 adaptation Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 230000035772 mutation Effects 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 abstract description 2
- 238000013473 artificial intelligence Methods 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000000034 method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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- G—PHYSICS
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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Abstract
A kind of adaptive algorithm for image recognition of the present invention belongs to intelligent image identification field.The following steps are included: step 1: acquisition image；Step 2: the image of acquisition is pre-processed；Step 3: will the pretreated image of progress described in the step 2, handled again, the image size that makes that treated is identical；Under a kind of adaptive algorithm effect for image recognition of the present invention, this algorithm carries out data analysis to image using principal component analysis, finds optimal characteristics, has unwanted visual characteristic removal for extra；Then benefit is generated algorithmically by initial population, and algorithm makes population be evenly distributed on domain, to promote whole search speed, improves using to genetic algorithm；Intersection and mutation probability according to fitness value adaptive change ensure population diversity, promote Searching efficiency and discrimination；Manual analysis and big data analysis comparing calculation are finally carried out, image object classification is carried out.
Description
Technical field
A kind of adaptive algorithm for image recognition of the present invention belongs to intelligent image identification field.
Background technique
The important component indispensable as following intelligent clothing monitoring system or artificial intelligence module, artificial intelligence
Energy image identification system has become the research hotspot of domestic and international developer.For electric business product identification system, most it is difficult to resolve
The electric business commodity that are how to be effectively treated certainly identify in real time and the problem of detection difficult.Artificial intelligence and corresponding machine learning
The research and development of algorithm provides new resolving ideas for the above problem, with linear discriminant analysis, neural network, subspace
Analysis, support vector machines, numerous machine learning algorithms that random forest is representative have been attempted and have identified applied to electric business commodity
In system.Result of study shows that all kinds of machine learning algorithms can greatly improve the discrimination of merchandise marks, but can also exist
The defects of computation complexity is high, live effect is poor, parameter setting solution is immature, the type of merchandise is limited is practical application
Bring no small obstruction.
Summary of the invention
In view of the above-mentioned problems, the invention discloses a kind of adaptive algorithms for image recognition.
The object of the present invention is achieved like this:
A kind of adaptive algorithm for image recognition, comprising the following steps:
Step 1: acquisition image；
Step 2: the image of acquisition is pre-processed；
Step 3: will the pretreated image of progress described in the step 2, handled again, the image size phase that makes that treated
Together；
Step 4: by size identical image described in the step 3, being carried out edge Edge contrast, operated using Binary Conversion,
By edgeization handle image, be converted into can digitized processing analysis image；
Step 5: image after will be processed described in the step 4 constructs 8 not bending moment formula using second-order moment around mean, together
When calculate image not bending moment；
Step 6: image after will be processed described in the step 5 carries out principal component analysis, forms new feature space；
Step 7: being combined using neural network algorithm and genetic algorithm, generates initial population；
Step 8: ideal adaptation angle value is calculated according to genetic algorithm calculation formula；
Step 9: by the step 8, calculated excellent individual carries out reservation operations；
Step 10: according to the step 7 algorithm calculation method, judge to stop output data；
Step 11: according to the step 10 output data, optimal characteristics data in image are selected, it is empty to form new data characteristics
Between；
Step 12: the data obtained to the step 11 carry out manual analysis and big data analysis comparing calculation, carry out figure
As target classification.
A kind of adaptive algorithm for image recognition, contribution rate is to percent 98 in the step 6.Described
A kind of adaptive algorithm for image recognition, the genetic algorithm are further comprising the steps of:
Step a, it is maximum to fitness value in population to record；
Step b, is calculated again, is obtained data greater than preceding population data, is replaced former data with big individual data items；Step c,
The current the smallest individual maximum individual replacement of fitness value of fitness value；
Step d guarantees that optimum individual is not destroyed.
The utility model has the advantages that
Under a kind of adaptive algorithm effect for image recognition of the present invention, this algorithm carries out image using principal component analysis
Data analysis, finds optimal characteristics, has unwanted visual characteristic removal for extra；Then benefit is generated algorithmically by initial population, and algorithm makes to plant
Group is evenly distributed on domain, to promote whole search speed, improves using to genetic algorithm；Certainly according to fitness value
The intersection and mutation probability for adapting to variation ensure population diversity, promote Searching efficiency and discrimination；Finally carry out manual analysis
With big data analysis comparing calculation, image object classification is carried out.
Specific embodiment
The specific embodiment of the invention is described in further detail below.
Specific embodiment one
The present embodiment is a kind of specific embodiment of the adaptive algorithm for image recognition of the present invention.
A kind of adaptive algorithm for image recognition, comprising the following steps:
Step 1: acquisition image；
Step 2: the image of acquisition is pre-processed；
Step 3: will the pretreated image of progress described in the step 2, handled again, the image size phase that makes that treated
Together；
Step 4: by size identical image described in the step 3, being carried out edge Edge contrast, operated using Binary Conversion,
By edgeization handle image, be converted into can digitized processing analysis image；
Step 5: image after will be processed described in the step 4 constructs 8 not bending moment formula using second-order moment around mean, together
When calculate image not bending moment；
Step 6: image after will be processed described in the step 5 carries out principal component analysis, forms new feature space；
Step 7: being combined using neural network algorithm and genetic algorithm, generates initial population；
Step 8: ideal adaptation angle value is calculated according to genetic algorithm calculation formula；
Step 9: by the step 8, calculated excellent individual carries out reservation operations；
Step 10: according to the step 7 algorithm calculation method, judge to stop output data；
Step 11: according to the step 10 output data, optimal characteristics data in image are selected, it is empty to form new data characteristics
Between；
Step 12: the data obtained to the step 11 carry out manual analysis and big data analysis comparing calculation, carry out figure
As target classification.
Specific embodiment two
The present embodiment is the specific embodiment that the present invention cures a kind of adaptive algorithm for image recognition.
A kind of adaptive algorithm for image recognition, contribution rate is to percent 98 in the step 6.
Specific embodiment three
The present embodiment is the specific embodiment that the present invention cures a kind of adaptive algorithm for image recognition.
A kind of adaptive algorithm for image recognition, the genetic algorithm are further comprising the steps of:
Step a, it is maximum to fitness value in population to record；
Step b, is calculated again, is obtained data greater than preceding population data, is replaced former data with big individual data items；Step c,
The current the smallest individual maximum individual replacement of fitness value of fitness value；
Step d guarantees that optimum individual is not destroyed.
Finally, it should be noted that these are only the preferred embodiment of the present invention, it is not intended to restrict the invention, although reference
Invention is explained in detail for previous embodiment, for those skilled in the art, still can be to aforementioned
Technical solution documented by each embodiment is modified or equivalent replacement of some of the technical features.
Claims (3)
1. a kind of adaptive algorithm for image recognition, which comprises the following steps:
Step 1: acquisition image；
Step 2: the image of acquisition is pre-processed；
Step 3: will the pretreated image of progress described in the step 2, handled again, the image size phase that makes that treated
Together；
Step 4: by size identical image described in the step 3, being carried out edge Edge contrast, operated using Binary Conversion,
By edgeization handle image, be converted into can digitized processing analysis image；
Step 5: image after will be processed described in the step 4 constructs 8 not bending moment formula using second-order moment around mean, together
When calculate image not bending moment；
Step 6: image after will be processed described in the step 5 carries out principal component analysis, forms new feature space；
Step 7: being combined using neural network algorithm and genetic algorithm, generates initial population；
Step 8: ideal adaptation angle value is calculated according to genetic algorithm calculation formula；
Step 9: by the step 8, calculated excellent individual carries out reservation operations；
Step 10: according to the step 7 algorithm calculation method, judge to stop output data；
Step 11: according to the step 10 output data, optimal characteristics data in image are selected, it is empty to form new data characteristics
Between；
Step 12: the data obtained to the step 11 carry out manual analysis and big data analysis comparing calculation, carry out figure
As target classification.
2. a kind of adaptive algorithm for image recognition according to claim 1, which is characterized in that in the step 6
Contribution rate is to percent 98.
3. a kind of adaptive algorithm for image recognition according to claim 1, which is characterized in that the genetic algorithm
It is further comprising the steps of:
Step a, it is maximum to fitness value in population to record；
Step b, is calculated again, is obtained data greater than preceding population data, is replaced former data with big individual data items；
Step c, the current the smallest individual maximum individual replacement of fitness value of fitness value；
Step d guarantees that optimum individual is not destroyed.
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| CN101465001A (en) * | 2008-12-31 | 2009-06-24 | 昆山锐芯微电子有限公司 | Method for detecting image edge based on Bayer RGB |
| CN101706335A (en) * | 2009-11-11 | 2010-05-12 | 华南理工大学 | Wind power forecasting method based on genetic algorithm optimization BP neural network |
| CN103942571A (en) * | 2014-03-04 | 2014-07-23 | 西安电子科技大学 | Graphic image sorting method based on genetic programming algorithm |
| CN104361352A (en) * | 2014-11-13 | 2015-02-18 | 东北林业大学 | Solid wood panel defect separation method based on compressed sensing |
| CN104504442A (en) * | 2014-12-30 | 2015-04-08 | 湖南强智科技发展有限公司 | Neural network optimization method |
| US9720934B1 (en) * | 2014-03-13 | 2017-08-01 | A9.Com, Inc. | Object recognition of feature-sparse or texture-limited subject matter |
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2019
- 2019-04-03 CN CN201910264244.3A patent/CN110059595A/en active Pending
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|---|---|---|---|---|
| CN101465001A (en) * | 2008-12-31 | 2009-06-24 | 昆山锐芯微电子有限公司 | Method for detecting image edge based on Bayer RGB |
| CN101706335A (en) * | 2009-11-11 | 2010-05-12 | 华南理工大学 | Wind power forecasting method based on genetic algorithm optimization BP neural network |
| CN103942571A (en) * | 2014-03-04 | 2014-07-23 | 西安电子科技大学 | Graphic image sorting method based on genetic programming algorithm |
| US9720934B1 (en) * | 2014-03-13 | 2017-08-01 | A9.Com, Inc. | Object recognition of feature-sparse or texture-limited subject matter |
| CN104361352A (en) * | 2014-11-13 | 2015-02-18 | 东北林业大学 | Solid wood panel defect separation method based on compressed sensing |
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Application publication date: 20190726 |