阅读说明：本技术 一种基于图像融合和改进ResNet的图像识别方法 (A kind of image-recognizing method based on image co-registration and improvement ResNet ) 是由 李建强 李鹏智 谢海华 王延安 于 2019-08-07 设计创作，主要内容包括：本发明涉及一种基于图像融合和改进ResNet的图像识别方法。本发明设计提取图像的多种特征,对大尺寸原图像进行主成分分析,分成从大到小的三个主成分向量,保留第一主成成分,即保留原图像的最大特征；然后对于两个较小的主成分分别用提取的两个特征进行替换,生成新的大尺寸图像。用新的大尺寸图像去训练改进的ResNet模型,从而提升模型对于尺寸比较大、光线强弱以及背景的干扰比较突出的图像识别分类的准确性和敏感度。(The present invention relates to a kind of based on image co-registration and improves the image-recognizing method of ResNet.The present invention designs the various features for extracting image, carries out principal component analysis to large scale original image, is divided into three principal component vectors from big to small, retains the first main Cheng Chengfen, that is, retains the maximum feature of original image；Then principal component lesser for two is replaced with two features extracted respectively, generates new large-size images.It is gone to train improved ResNet model with new large-size images, so that lift scheme is bigger for size, the accuracy and susceptibility of the interference of light intensity and background image recognition classification more outstanding.)

1. a kind of image-recognizing method based on image co-registration and improvement ResNet, which is characterized in that

Specifically includes the following steps:

(1) image training sample is made:

(1) -1 pair of training image data acquisition system carries out associated specialist label, forms image training sample set, original image is r*c The RGB image of size；

(2) whitening processing and extraction LBP feature:

(2) -1 extract albefaction feature and LBP feature using existing digital image processing method for original image respectively,

(3) principal component analysis replacement is handled:

(3) -1, which read former RGB image, obtains corresponding matrix, is denoted as mol₀, i.e. mol₀Form be r*c*3；Read albefaction image Matrix is r*c, is denoted as ban₀；Reading LBP matrix is r*c, is denoted as ban01₀；Then matrix data normalization pretreatment is carried out to exist Between 0-1: formula (1) is as follows:

Mol=mol₀/255；Ban=ban₀/255；Ban01=ban01₀/255 (1)

(3) -2 the matrixing of mol at the matrix of (r*c) * 3, i.e., in a row each access matrix transformation of mol Example, is denoted as mols, then finds out the correlation matrix of mol, be denoted as correlation, and formula (2) is as follows, and wherein mol ' is mol's Transposed matrix；Then it finds out characteristic value value and feature vector vector obtains main principal component matrix, be denoted as pa, formula (3) is such as Under；Then obtained principal component matrixing is denoted as pas at the matrix of a r*c*3；

(mol ' * mol)/(r*c) (2) correlation=

Pa=mols*vector (3)

(3) -3 because the characteristic value value that second step obtains is arranged from small arrival, and the third channel of pas is exactly First principal component retains first principal component, remaining two lesser channels is normalized, i.e., the member in each channel Then element respectively carries out the feature of whitening processing and LBP feature with remaining two lesser channel principal components between 0-1 Histogram Matching processing, even if the histogram of ban, ban01 are consistent with the histogram of Second principal component, third principal component；

After the completion of (3) -4 Histogram Matchings, ban, ban01 go to replace second and third main matrix at ingredient respectively, then pass through PCA inverse transformation reconstruct blending image forms new RGB image；

(4) ResNet is improved:

The convolution of (4) -1 reserving models is motionless, removes the last average pond layer of model and full articulamentum obtains r characteristic pattern, That is then feature maps sequentially adds Relu layers, convolutional layer, wherein convolution kernel size is 3*3, and step-length 2, convolutional layer obtains R characteristic pattern after to down-sampling, adds batch processing standardization layer, i.e. Batch Normalization, and average pond layer obtains To the characteristic pattern of r 1*1, it is then converted to the vector of r*1, finally obtains classification results by full articulamentum；

(5) the improved ResNet model of training:

(5) -1 directly go to train improved ResNet model with newly-generated big image training sample set, obtain final figure As identification classification results.

Technical field

The present invention design extract image various features, to large scale original image carry out principal component analysis, be divided into from greatly to Three small principal component vectors retain the first main Cheng Chengfen, that is, retain the maximum feature of original image；Then lesser for two Principal component is replaced with two features extracted respectively, generates new large-size images.It goes to train with new large-size images Improved ResNet model, so that lift scheme is bigger for size, the interference of light intensity and background is more outstanding The accuracy and susceptibility of image recognition classification.

Background technique

Image recognition is a key areas of artificial intelligence, special according to the difference reflected in each comfortable image information Sign, to the image processing method of target area identification classification.The conventional method of image recognition is feature description and detection, but due to reality Border situation is extremely complex, and traditional classification method can't bear the heavy load.Now popular is certain CNN model to carry out image Identification classification, such as ResNet.The training for the acceleration deep-neural-network that the structure of ResNet can be exceedingly fast, the accuracy rate of model There is bigger promotion.But when training the image of larger size with ResNet, image will generally be zoomed in and out, with this Guarantee that last feature maps size dimension is suitable, as 512X512 size image scaling at 224X224 size figure Picture, scaling excessive in this way, which will lead to some details of image impairment, influences accuracy of identification.Secondly as the background of shooting image With the variation of light intensity, accurately identification classification or it is highly difficult.Currently, the image bigger for size, light are strong The accuracy and sensibility of weak and background interference image recognition classification more outstanding optimize up for improving.

Summary of the invention

Present invention design carries out whitening processing for raw image data and extracts LBP textural characteristics, then utilizes PCA pairs Original RGB image carries out principal component analysis, successively obtains three principal component vectors from small to large, then retain first it is big it is main at Point, with whitening processing feature and LBP feature carry out replacing after HiBtogram matching respectively lesser two principal components formed it is new Image.The main information that original image can thus be retained replaces some noise informations in image.Secondly, for ResNet model removes model included average pond layer and full articulamentum, then sequentially add Relu layers, 3*3 convolutional layer, Normalization layers of Batch, average pond layer, full articulamentum improve existing model with this, and raising is to large-size images Adaptability.It finally goes to train improved ResNet model with the new training sample set formed, obtains final knowledge Other classification results, to promote the interference image recognition more outstanding point of, light intensity bigger for size and background The accuracy and susceptibility of class.

Present invention employs the following technical solution and realize step:

1. a kind of image-recognizing method based on image co-registration and improvement ResNet, which is characterized in that

Specifically includes the following steps:

(1) image training sample is made:

(1) -1 pair of training image data acquisition system carries out associated specialist label, forms image training sample set, and original image is The RGB image of r*c size；

(2) whitening processing and extraction LBP feature:

(2) -1 extract albefaction feature and LBP spy using existing digital image processing method for original image respectively Sign,

(3) principal component analysis replacement is handled:

(3) -1, which read former RGB image, obtains corresponding matrix, is denoted as mol₀, i.e. mol₀Form be r*c*3；Read albefaction Image array is r*c, is denoted as ban₀；Reading LBP matrix is r*c, is denoted as ban01₀；Then the pre- place of matrix data normalization is carried out Reason is between 0-1: formula (1) is as follows:

Mol=mol₀/255；Ban=ban₀/255；Ban01=ban01₀/255 (1)

(3) -2 the matrixing of mol at the matrix of (r*c) * 3, i.e., each access matrix of mol is transformed into one Row example, is denoted as mols, then finds out the correlation matrix of mol, is denoted as correlation, and formula (2) is as follows, and wherein mol ' is mol Transposed matrix；Then it finds out characteristic value value and feature vector vector obtains main principal component matrix, be denoted as pa, formula (3) It is as follows；Then obtained principal component matrixing is denoted as pas at the matrix of a r*c*3；

(mol ' * mol)/(r*c) (2) correlation=

Pa=mols*vector (3)

(3) -3 because the characteristic value value that second step obtains is arranged from small arrival, the third channel of pas It is exactly first principal component, retains first principal component, remaining two lesser channels are normalized, i.e., each channel Element be between 0-1, then the feature of whitening processing and LBP feature respectively with remaining two lesser channel principal components Histogram Matching processing is carried out, even if the histogram of ban, ban01 are consistent with the histogram of Second principal component, third principal component；

After the completion of (3) -4 Histogram Matchings, ban, ban01 go to replace second and third main matrix at ingredient respectively, then lead to It crosses PCA inverse transformation reconstruct blending image and forms new RGB image；

(4) ResNet is improved:

The convolution of (4) -1 reserving models is motionless, removes the last average pond layer of model and full articulamentum obtains r feature Then figure, i.e. feature maps sequentially add Relu layers, convolutional layer, wherein convolution kernel size is 3*3, step-length 2, convolution Layer obtains r characteristic pattern after down-sampling, adds batch processing standardization layer, i.e. Batch Normalization, average pond Layer obtains the characteristic pattern of r 1*1, is then converted to the vector of r*1, finally obtains classification results by full articulamentum；

(5) the improved ResNet model of training:

(5) -1 directly go to train improved ResNet model with newly-generated big image training sample set, obtain final Image recognition classification results.

Creativeness of the invention is mainly reflected in:

A large amount of sample image can all know image by the interference of take pictures light intensity and background in actual life The accuracy and sensibility that do not classify produce large effect.The whitening processing of image can obtain not strong by ambient lighting The constant information that those of multifactor influences such as degree, object reflection, shooting camera are not influenced by the external world；Image LBP feature is come The operator of image local textural characteristics is described, it has the advantages that rotational invariance and gray scale invariance etc. are significant.Utilize PCA handle The lesser principal component of original image is replaced, and it is aobvious to increase the image recognition feature under complex environment to form new RGB image It is existing.For new RGB image without scaling processing, is directly gone to train improved ResNet with large-size images, reduce image Loss of significance improves the accuracy and sensibility of image recognition classification.

The present invention design extract image various features, to large scale original image carry out principal component analysis, be divided into from greatly to Three small principal component vectors retain the first main Cheng Chengfen, that is, retain the maximum feature of original image；Then lesser for two Principal component is matched and then is replaced with the advanced column hisgram of two features extracted respectively, generates new large-size images.With New large-size images directly go to train improved ResNet model without scaling processing, so that lift scheme is for size ratio The accuracy and susceptibility of the interference of larger, light intensity and background image recognition classification more outstanding.

Detailed description of the invention

Fig. 1 is overall procedure block diagram of the present invention.

Fig. 2 is that the present invention is principal component analysis replacement treatment process subgraph

Fig. 3 is that the present invention is ResNet model refinement process subgraph

Specific embodiment

1. present invention design combines image interfusion method to be promoted with improvement ResNet model, bigger for size, light is strong The classification results of weak and background interference image more outstanding, it is characterised in that: at production image training book, albefaction Reason and extraction LBP feature, principal component analysis replacement processing, improve ResNet, the improved ResNet model of training

Specifically includes the following steps:

(1) image training sample is made:

(1) -1 pair of training image data acquisition system carries out associated specialist label, forms image training sample set, such as original image It seem the RGB image of 512*512.

(2) whitening processing and extraction LBP feature:

(2) -1 extract albefaction feature and LBP spy using existing digital image processing method for original image respectively Sign

(3) principal component analysis replacement is handled:

(3) -1 reading original images obtain corresponding matrix (512*512*3) record and are mol, i.e. the size of mol is r*c* band；It reads albefaction image array (512*512) and is denoted as ban；It reads LBP matrix (512*512) and is denoted as ban01；Then it carries out Matrix data normalization pretreatment is between 0-1:

(3) -2 the matrix of mono- 262144*3 of matrix r eshape of mol (wherein 262144 be exactly r*c result), note For mols；I.e. in a row each channel reshape of mol；Then the correlation matrix for finding out mol, is denoted as correlation, Formula (2) is following (the wherein transposed matrix that mol ' is mol)；Then characteristic value value and feature vector vector is found out to obtain Main principal component matrix, is denoted as pa, and formula (3) is as follows；Then obtained principal component matrix r eshape at a 512*512*3 Matrix, be denoted as pas

/ 262144 (2) correlation=(mol ' * mol)

Pa=mols*vector (3)

(3) -3 because the characteristic value value that second step obtains is arranged from small arrival, the third channel of pas It is exactly first principal component, retains first principal component, remaining two lesser channels are normalized, i.e., each channel Element be between 0-1, then the feature of whitening processing and LBP feature respectively with remaining two lesser channel principal components Histogram Matching processing is carried out, even if the histogram of ban, ban01 are consistent with the histogram of Second principal component, third principal component

After the completion of (3) -4 Histogram Matchings, ban, ban01 go to replace second and third main matrix at ingredient respectively, then lead to It crosses PCA inverse transformation reconstruct blending image and forms new RGB image.

(4) ResNet is improved:

The operation such as convolution of (4) -1 reserving models is motionless, removes the last average pond layer of model and full articulamentum obtains Feature maps (512*16*16) then sequentially adds Relu layers, 3*3 convolutional layer (kerner size=(3,3) stride =(2,2) padding=1) feature maps (512*8*8) is obtained, it adds Normalization layers of Batch, be averaged Pond layer obtains feature maps (512*1*1), and then resize is finally obtained by full articulamentum at the vector of 512*1 Classification results.

(5) the improved ResNet model of training:

(5) -1 directly go to train improved ResNet model with new image training sample set (512*512 size), obtain To final image recognition classification results.

More particularly:

(1) image training sample is made:

(1) -1 pair of training image data acquisition system carries out associated specialist label, forms image training sample set, such as original image It seem the RGB image of 512*512.

(2) whitening processing and extraction LBP feature:

(2) -1 extract albefaction feature and LBP spy using existing digital image processing method for original image respectively Sign

(3) principal component analysis replacement is handled:

(3) -1 reading original images obtain corresponding matrix (512*512*3) record and are mol, i.e. the size of mol is r*c* band；It reads albefaction image array (512*512) and is denoted as ban；It reads LBP matrix (512*512) and is denoted as ban01；Then it carries out Matrix data normalization pretreatment is between 0-1:

(3) -2 the matrix of mono- 262144*3 of matrix r eshape of mol (wherein 262144 be exactly r*c result), note For mols；I.e. in a row each channel reshape of mol；Then the correlation matrix for finding out mol, is denoted as correlation, Formula (2) is following (the wherein transposed matrix that mol ' is mol)；Then characteristic value value and feature vector vector is found out to obtain Main principal component matrix, is denoted as pa, and formula (3) is as follows；Then obtained principal component matrix r eshape at a 512*512*3 Matrix, be denoted as pas

/ 262144 (2) correlation=(mol ' * mol)

Pa=mols*vector (3)

(3) -3 because the characteristic value value that second step obtains is arranged from small arrival, the third channel of pas It is exactly first principal component, retains first principal component, remaining two lesser channels are normalized, i.e., each channel Element be between 0-1, then the feature of whitening processing and LBP feature respectively with remaining two lesser channel principal components Histogram Matching processing is carried out, even if the histogram of ban, ban01 are consistent with the histogram of Second principal component, third principal component

After the completion of (3) -4 Histogram Matchings, ban, ban01 go to replace second and third main matrix at ingredient respectively, then lead to It crosses PCA inverse transformation reconstruct blending image and forms new RGB image.

(4) ResNet is improved:

The operation such as convolution of (4) -1 reserving models is motionless, removes the last average pond layer of model and full articulamentum obtains Feature maps (512*16*16) then sequentially adds Relu layers, 3*3 convolutional layer (kerner size=(3,3) stride =(2,2) padding=1) feature maps (512*8*8) is obtained, it adds Normalization layers of Batch, be averaged Pond layer obtains feature maps (512*1*1), and then resize is finally obtained by full articulamentum at the vector of 512*1 Classification results.

(5) the improved ResNet model of training:

(5) -1 directly go to train improved ResNet model with new image training sample set (512*512 size), obtain To final image recognition classification results.