CN111862178A - Improved LBP feature extraction method - Google Patents
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Abstract
The invention discloses an improved LBP (local binary pattern) feature extraction method, which comprises the following steps: acquiring an image data set to be detected according to an actual detection process; generating a training image set, respectively performing frame selection on the features to be detected in each image, and recording a width pixel value and a height pixel value; clustering processing is carried out by taking the k clustering results as a sample data set to obtain k clustering results; using reference width value mu of single clustering result respectivelyiAnd a reference height value giCalculating the radius RiObtaining a radius set; taking each value in the radius set as the radius of the circular neighborhood of the LBP respectively to form q LBP feature description operators; respectively traversing the image I by using q LBP feature description operators to obtain q feature vectors, cascading to obtain a fusion feature vector, recording the fusion feature vector as the feature description of the image I, and finishing the extraction of the LBP feature; the method has the advantages of more accurate identification of the target characteristics and improvement of the accuracy of the identification algorithm.
Description
Technical Field
The invention relates to the field of image processing, in particular to an improved LBP feature extraction method.
Background
With the development of digital image processing technology, the digital processing technology has replaced manual methods to perform automatic feature recognition and analysis, such as face recognition, fingerprint indexes, defect detection, and the like, feature extraction is firstly performed in the feature recognition, wherein, an LBP (Local Binary pattern) feature extraction method has been widely applied in the fields of face recognition, image quality evaluation, target detection, and the like, the traditional LBP method sets a 3 × 3 square neighborhood, then gradually derives a circular field, and the improved LBP operator allows any number of pixel points to be in the circular neighborhood with the radius of R, thereby obtaining the LBP operator which contains P sampling points in the circular region with the radius of R; however, the determination of the radius R requires repeated verification by scientific researchers, and meanwhile, the sizes corresponding to different features are different, so that various features cannot be accurately extracted by a single set R value, and false detection and missing detection are easy to occur.
Disclosure of Invention
In order to solve the problems, the invention provides an improved LBP feature extraction method, which combines a clustering method to obtain a radius set, designs an LBP descriptor, and uses a multi-scale LBP operator to jointly detect the same image to be detected to form a fusion feature vector.
The technical scheme is as follows:
an improved LBP feature extraction method comprises the following steps:
acquiring images I of a plurality of objects to be detected according to an actual detection process, and recording the images I as an image data set to be detected;
all or part of images in the image data set to be detected are used as a training image set to ensure that the training image set contains all types of characteristics to be detected; respectively selecting the features to be detected in each image, and recording the width pixel value and the height pixel value of each feature to be detected;
clustering each feature to be measured and the width pixel value and the height pixel value of the feature to be measured as a sample data set to obtain k clustering results, wherein each single clustering result comprises a reference width value muiAnd a reference height value gi;
Step two, respectively utilizing the reference width value mu of the single clustering resultiAnd a reference height value giCalculating the radius R of the detection range of the LBP feature description operator corresponding to the feature to be detectediThe method comprises the following steps:
wherein: i is 1,2 … … k; deleting the repeated data to obtain a radius set R ═ { R ═ R1,R2,…,Rq};
Step three, taking each value in the radius set as the radius of a circular neighborhood of the LBP respectively to form q LBP feature description operators;
and step four, traversing the image I by using q LBP feature description operators respectively to obtain q feature vectors, cascading to obtain a fusion feature vector, and recording the fusion feature vector as the feature description of the image I to finish the extraction of the LBP feature.
Step five, inputting the fusion feature vector into a classifier or a convolutional neural network for feature recognition and classification; and obtaining the characteristics to be detected in the image I.
Preferably, the clustering method in the first step is a K-means clustering method, and the K value is equal to the number of the types of the features to be detected.
Further, the feature to be measured in the step one is a local feature (e.g. facial feature) of the object to be measured itself, or a defect feature (e.g. scratch, impurity) of the surface of the object to be measured.
Further, in step four, the step of traversing the image I by using a single LBP feature description operator is:
I. setting a local circular area from the starting point of the image I according to the radius of the detection range of the current single LBP feature description operator; presetting the number P of sampling points of a local circular area;
II. Calculate each sample point (x) within a local circular regionp,yp):
Wherein (A), (B), (C), (D), (C), (xc,yc) As the pixel coordinate of the center point of the current circular region, RiRepresents the radius of the detection range of the current single LBP feature description operator, P is 1,2 … … P;
then, processing P sampling points (x) by using bilinear interpolation methodp,yp) Obtaining the pixel coordinates of each sampling point;
noting the center point (x)c,yc) Comparing the gray value at the pixel coordinate of the sampling point with A, wherein the gray value is A, the gray value at the pixel coordinate of the sampling point is larger than A, the sampling point is marked as 1, otherwise, the sampling point is marked as 0, and a group of binary data is obtained;
III, moving the local circular area along the X-axis/Y-axis direction of the image I, and repeating the step II; and cascading all the obtained groups of binary data until the whole image I is traversed, and recording the binary data as a feature vector.
Preferably, the number of sample points P is 8, 16 or 32.
In order to increase the calculation speed, the feature vector is preferably subjected to dimension reduction processing by using an equivalence mode, and the feature dimension is reduced to P × (P-1) +3 dimensions.
Equivalent mode: when a cyclic binary number corresponding to a certain LBP has two jumps from 0 to 1 or from 1 to 0 at most, the type is reserved; when the jumping times exceed 2 times, the Chinese characters are classified into one class; the total 58 types of 8-bit binary numbers with jump less than or equal to two times.
This way, the amount of calculation can be greatly reduced, and information can not be lost. For 8 sampling points in a 3 × 3 neighborhood, the binary pattern is reduced from the original 256 to 59, which makes the feature vector have fewer dimensions and reduces the effect of high-frequency noise.
The scheme of the invention has the following advantages: the method combines a clustering method, considers different feature sizes, obtains a radius set, designs LBP descriptors by using different radius values respectively, and detects the same image to be detected by using a multi-scale LBP operator together to form a fusion feature vector.
Drawings
FIG. 1 is a schematic flow diagram of the inventive process;
FIG. 2 is a graph of the original gray scale of an automobile paint panel member;
fig. 3 is an LBP feature map obtained by traversing the entire image to be detected by using LBP feature description operators (R ═ 1,2, and 3) with different radius values, respectively;
fig. 4 is an LBP feature map obtained by traversing the entire image to be detected by using LBP feature description operators (R ═ 5, 7) with different radius values, respectively;
FIG. 5 is a diagram of classification results input into an SVM after features are extracted using a conventional circular neighborhood LBP method;
fig. 6 is a diagram of the classification results input into the SVM after the features are extracted by the LBP method of the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings and the detailed description.
An improved LBP feature extraction method, as shown in fig. 1, includes the following steps:
acquiring images I of a plurality of objects to be detected according to an actual detection process, and recording the images I as an image data set to be detected;
all or part of images in the image data set to be detected are used as a training image set to ensure that the training image set contains all types of characteristics to be detected; respectively selecting the features to be detected in each image, and recording the width pixel value and the height pixel value of each feature to be detected;
clustering each feature to be measured and the width pixel value and the height pixel value of the feature to be measured as a sample data set to obtain k clustering results, wherein each single clustering result comprises a reference width value muiAnd a reference height value gi;
Step two, respectively utilizing the reference width value mu of the single clustering resultiAnd a reference height value giCalculating the radius R of the detection range of the LBP feature description operator corresponding to the feature to be detectediThe method comprises the following steps:
wherein: i is 1,2 … … k; deleting the repeated data to obtain a radius set R ═ { R ═ R1,R2,…,Rq};
Step three, taking each value in the radius set as the radius of a circular neighborhood of the LBP respectively to form q LBP feature description operators;
and step four, traversing the image I by using q LBP feature description operators respectively to obtain q feature vectors, cascading to obtain a fusion feature vector, and recording the fusion feature vector as the feature description of the image I to finish the extraction of the LBP feature.
Inputting the fusion feature vector into a classifier or a convolutional neural network, and performing feature identification and classification; and obtaining the characteristics to be detected in the image I.
As an implementation mode of the invention, the clustering method in the step one is a K-means clustering method, and the K value is equal to the number of the types of the features to be measured.
Specifically, the characteristic to be measured in the step one is a local characteristic (such as a feature of five sense organs in a human face) of the object to be measured, or a defect characteristic (such as a scratch and an impurity) of the surface of the object to be measured.
In this embodiment, the camera forms an image data set to be detected from an image of the painted surface of the body panel shot at the automobile detection station, and selects 70% of the images as a training image set, so that 7 defect categories are defined: impurities (Inclusion), Shallow cavities (Shallow crater), pinholes (Pinhole crater), depressions (Dent), bumps (Bump), sagging (Boilers), scratches (Scratch); marking width pixel values and height pixel values of each image with defects in the training image set;
setting K to 7, forming 7 sets of reference width values muiAnd a reference height value gi。
Obtaining a radius set R ═ {1,2,3,5,7} in the second step;
in the fourth step, the step of traversing the image I by using a single LBP feature description operator is as follows:
I. setting a local circular area from the starting point of the image I according to the radius of the detection range of the current single LBP feature description operator; presetting the number P of sampling points of a local circular area;
II. Calculate each sample point (x) within a local circular regionp,yp):
Wherein (x)c,yc) As the pixel coordinate of the center point of the current circular region, RiRepresents the radius of the detection range of the current single LBP feature description operator, P is 1,2 … … P;
then, processing P sampling points (x) by using bilinear interpolation methodp,yp) Obtaining the pixel coordinates of each sampling point;
noting the center point (x)c,yc) Comparing the gray value at the pixel coordinate of the sampling point with A, wherein the gray value is A, the gray value at the pixel coordinate of the sampling point is larger than A, the sampling point is marked as 1, otherwise, the sampling point is marked as 0, and a group of binary data is obtained;
III, moving the local circular area along the X-axis/Y-axis direction of the image I, and repeating the step II; and cascading all the obtained groups of binary data until the whole image I is traversed, and recording the binary data as a feature vector.
Wherein, the number of sampling points P is 8, 16 or 32.
In order to improve the calculation speed, the feature vector is subjected to dimension reduction processing by adopting an equivalent mode, and the feature dimension is reduced to P x (P-1) +3 dimensions.
During the actual use, to same detection station, step one ~ step four only need be gone on once, obtain radius set R and generate q LBP operator after, q LBP operator can directly be used for subsequent real-time detection process, and specific real-time detection process is: the camera collects the painting surface image of the automobile body panel at the same station in real time (as shown in fig. 2), the image is directly subjected to the fourth step, wherein LBP characteristic diagrams obtained by different LBP operators are shown in fig. 3 and 4, the defect characteristics in the diagrams can be seen as black and gray impurities (embodied as black dots in the diagrams) and natural scratch (scratch on the lower right side of the diagrams)), and the LBP operators with different scales can be respectively adopted to extract more comprehensive characteristic information; the feature vectors extracted by the method and the feature vectors obtained by adopting the traditional circular neighborhood LBP method are respectively input into an SVM (Support Vector Machine), the classification result graphs are shown as figures 5 and 6, and as can be seen from the figures, the defect feature extraction is more accurate and comprehensive in the classification graphs processed by the method, and the phenomena of missing detection and error detection easily occur by adopting the traditional method.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. The foregoing description is not intended to be exhaustive or to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable others skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications thereof. It is intended that the scope of the invention be defined by the following claims and their equivalents.
Claims (7)
1. An improved LBP feature extraction method is characterized by comprising the following steps:
acquiring images I of a plurality of objects to be detected according to an actual detection process, and recording the images I as an image data set to be detected;
all or part of images in the image data set to be detected are used as a training image set to ensure that the training image set contains all types of characteristics to be detected; respectively selecting the features to be detected in each image, and recording the width pixel value and the height pixel value of each feature to be detected;
clustering each feature to be measured and the width pixel value and the height pixel value of the feature to be measured as a sample data set to obtain k clustering results, wherein each single clustering result comprises a reference width value muiAnd a reference height value gi;
Step two, respectively utilizing the reference width value mu of the single clustering resultiAnd a reference height value giCalculating the radius R of the detection range of the LBP feature description operator corresponding to the feature to be detectediThe method comprises the following steps:
wherein: i is 1,2 … … k; deleting the repeated data to obtain a radius set R ═ { R ═ R1,R2,…,Rq};
Step three, taking each value in the radius set as the radius of a circular neighborhood of the LBP respectively to form q LBP feature description operators;
and step four, traversing the image I by using q LBP feature description operators respectively to obtain q feature vectors, cascading to obtain a fusion feature vector, and recording the fusion feature vector as the feature description of the image I to finish the extraction of the LBP feature.
2. The improved LBP feature extraction method as claimed in claim 1, further comprising a fifth step of inputting said fused feature vector into a classifier or a convolutional neural network for feature recognition and classification; and obtaining the characteristics to be detected in the image I.
3. The improved LBP feature extraction method of claim 1, wherein the clustering process in step one is a K-means clustering process, where the K value is equal to the number of classes of the features to be tested.
4. The improved LBP feature extraction method of claim 1, wherein the feature to be tested in step one is a local feature of the object to be tested itself or a defect feature of the surface of the object to be tested.
5. The improved LBP feature extraction method of claim 1, wherein in step four, the step of traversing the image I with a single LBP feature description operator is:
I. setting a local circular area from the starting point of the image I according to the radius of the detection range of the current single LBP feature description operator; presetting the number P of sampling points of a local circular area;
II. Calculate each sample point (x) within a local circular regionp,yp):
Wherein (x)c,yc) As the pixel coordinate of the center point of the current circular region, RiRepresents the radius of the detection range of the current single LBP feature description operator, P is 1,2 … … P;
then, processing P sampling points (x) by using bilinear interpolation methodp,yp) Obtaining the pixel coordinates of each sampling point;
noting the center point (x)c,yc) Comparing the gray value at the pixel coordinate of the sampling point with A, wherein the gray value is A, the gray value at the pixel coordinate of the sampling point is larger than A, the sampling point is marked as 1, otherwise, the sampling point is marked as 0, and a group of binary data is obtained;
III, moving the local circular area along the X-axis/Y-axis direction of the image I, and repeating the step II; and cascading all the obtained groups of binary data until the whole image I is traversed, and recording the binary data as a feature vector.
6. The improved LBP feature extraction method of claim 5, wherein: the number of sample points P is 8, 16 or 32.
7. The improved LBP feature extraction method of claim 5, wherein the feature vector is dimension-reduced by using an equivalence mode to reduce the feature dimension to P x (P-1) +3 dimensions.
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Address after: Room 495, building 3, 1197 Bin'an Road, Binjiang District, Hangzhou City, Zhejiang Province 310051 Patentee after: Yi Si Si (Hangzhou) Technology Co.,Ltd. Address before: Room 495, building 3, 1197 Bin'an Road, Binjiang District, Hangzhou City, Zhejiang Province 310051 Patentee before: ISVISION (HANGZHOU) TECHNOLOGY Co.,Ltd. |