CN104091174A - Portrait style classification method based on support vector machine - Google Patents

Abstract

The invention discloses a portrait style classification method based on a support vector machine. The method mainly solves the problems that professional physical devices are required in the famous painting discrimination process, and the accuracy of criminal suspect photos which are compared in the criminal investigation and case cracking process is poor. According to the technical scheme, (1) a database sample set is divided into a training set and a testing set; (2) the training set is divided into five component training sets; (3) five groups of style sets and corresponding class labels are generated through each component training set; (4) support vector parameters are generated between the five groups of style sets and the corresponding class labels; (5) the testing set is divided into five component testing sets; (6) a component vector is generated through each component testing set; (7) five component class labels are generated through the five component vectors; (8) a style class label of a tested portrait is generated through the five component class labels. By means of the portrait style classification method based on the support vector machine, the style of the portrait can be discriminated without the professional physical devices, the criminal suspect photos with high accuracy can be obtained through comparison, and the method can be applied to famous painting discrimination, criminal investigation and case cracking.

Description

Portrait genre classification method based on Support Vector Machine

Technical field

The invention belongs to technical field of image processing, further relate to the portrait genre classification method in pattern-recognition and technical field of computer vision, can be used for famous painting and screen and criminal investigation and case detection.

Background technology

Being sorted in famous painting examination and criminal investigation and case detection of portrait style plays an important role at present.Screen field at famous painting, public and private hidden famous painting enormous amount handed down from ancient times both at home and abroad, their situation is very complicated, some true some false, some anonymities, need to carry out strict scientific verification to it.In recent years art history scholar more and more can utilize the science tools qualification artwork true and false, and their conventional authenticate technology has isotope analysis technology and infrared external reflection imaging technique.Wherein infrared external reflection imaging authenticate technology is on the paintings that first need to identify with infrared radiation, then utilizes thermal infrared imager to help qualification.After infrared penetration is pigment coated, absorbed by the material of initial profile rough draft, because rough draft only reflects little heat, on infrared image, present the black lines of rough draft.When everyone draws rough draft, there is the drawing style of oneself, just can disclose the true and false of paintings by the style of rough draft.But these authenticate technologies are all physical analysis means, when qualification, need professional equipment, this has brought inconvenience with regard to the qualification of giving famous painting.

In criminal investigation and case detection, be generally by artist by paint out suspect's portrait of eye witness's description, then portrait is put into photomontage in sketch-photo database, then compares out suspect's photo with the photo in picture data storehouse.Due to the portrait style difference in sketch-photo database, make this database there is diversity, photomontage is not accurate, suspect's photo poor accuracy of comparing out.

Summary of the invention

The object of the invention is to propose a kind of portrait genre classification method based on Support Vector Machine, to assist expert and amateur identify the author of portrait and help criminal investigation and case detection personnel to compare out suspect's photo that accuracy is high.

For achieving the above object, technical scheme of the present invention comprises the steps:

(1) partition database sample set: portrait collection is divided into training set U={U _p, p=1,2 ..., 150} and test set wherein U _prepresent that the p in training set opens portrait, represent that the q in test set opens portrait;

(2) divide training set: the difference that pending portrait training set U is pressed to parts, is divided into five training component collection respectively face, left eye, right eye, nose and nozzle component training set, represent that training set p opens i parts of portrait;

(3) at each parts training set U ⁱ, i=1,2 ..., generate five groups of style collection and corresponding class mark on 5;

(4) integrate and generate support vector parameter between corresponding class mark five groups of styles: first for five groups of style collection of step (3) and accordingly class mark respectively as the input and output of Support Vector Machine, between generates one group of member supporting vector parameter again, five corresponding five groups of member supporting vector parameters of parts;

(5) divide test set: by pending portrait test set press the difference of parts, be divided into five test component collection respectively face, left eye, right eye, nose and nozzle component test set, represent that respectively test set q opens i parts of portrait;

(6) at each unit test collection upper generation test component vector set

(7) with every five test component vector sets that test is drawn a portrait in step (6) and five member supporting vector parameters of correspondence in step (4), as the input of Support Vector Machine, obtain five parts class marks;

(8), by five parts class marks of step (7), by the Voting principle of " the minority is subordinate to the majority ", obtain the style class mark of test portrait.

Tool of the present invention has the following advantages:

The first, the present invention is owing to having considered face, left eye, and right eye, five parts of nose and mouth, and extracted four features relevant with portrait style: grey level histogram, Gray Moment, SURF and LBP feature, the classification of style of making to draw a portrait is more accurate;

The second, the present invention, due to the style that adopts the classification of Support Vector Machine to draw a portrait, is applicable to solve the such small sample problem of portrait genre classification;

The 3rd, the present invention uses mathematical model directly to portrait genre classification first, does not distinguish portrait style by means of professional physical equipment, not only convenient operation, and also the suspicion of crime photo of comparing out is more accurate.

Brief description of the drawings

Fig. 1 is the process flow diagram that the present invention is based on the portrait genre classification method of Support Vector Machine.

Embodiment

Core concept of the present invention is: the thought by Support Vector Machine proposes a kind of sorting technique of drawing a portrait style, to assist expert and amateur identify the author of portrait and help criminal investigation and case detection personnel to compare out suspect's photo that accuracy is high.Below provide an example:

With reference to Fig. 1, the concrete implementation step of the present invention is as follows:

Step 1, partition database sample set.

One group in pending VIPSL database 200 portrait data sets that comprise 5 artists are divided into training set U={U _p, p=1,2 ..., 150} and test set wherein U _prepresent that the p in training set opens portrait, represent that the q in test set opens portrait.

Step 2, divides training set.

The difference that pending portrait training set U is pressed to parts, is divided into five training component collection respectively face, left eye, right eye, nose and nozzle component training set, represent that training set p opens i parts of portrait:

(2a) using the former figure of every portrait of training set U as face's part, size is made as 176x239, using face's part of all portraits of training set U as the part training set U of face ¹;

(2b) centered by the pupil of left eye of every portrait of training set U, the square chart picture that to get size be 30x22 is as left eye parts, using the left eye parts of all portraits of training set U as left eye parts training set U ²;

(2c) centered by the pupil of right eye of every portrait of training set U, the square chart picture that to get size be 30x22 is as right eye parts, using the right eye parts of all portraits of training set U as right eye parts training set U ³;

(2d) by the center of two interpupillary lines of every portrait of training set U centered by the intermediate point of nose, the square chart picture that to get size be 30x22 is as nose piece, using the nose piece of all portraits of training set U as nose piece training set U ⁴;

(2e) centered by the face center of every portrait of training set U, the square chart picture that to get size be 30x22 is as nozzle component, using the nozzle component of all portraits of training set U as nozzle component training set U ⁵.

Step 3, at the training set U of each parts ⁱ, i=1,2 ..., generate five groups of style collection and corresponding class mark on 5.

(3a) by each parts training set U ⁱin each parts be divided into training square block:

(3a1) by the part training set U of face ¹face's part be divided into size for the training square block of 32x32, the square block that the lap between piece is 16x16;

(3a2) by left eye parts training set U ²left eye parts be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a3) by right eye parts training set U ³right eye parts be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a4) by nose piece training set U ⁴nose piece be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a5) by nozzle component training set U ⁵nozzle component be divided into size for the training square block of 22x22, the lap size between piece is the square block of 11x11;

(3b) on each training square block, generate training feature vector V _f:

(3b1) on each training square block, extract grey level histogram feature, the pixel of the 0-255 of an each training square block gray level is counted, obtain a dimension and be 256 training grey level histogram proper vector V ₁, the numerical value of every dimension is the pixel quantity of this gray level;

(3b2) on each training square block, extract gray Moment Feature, each training square block is calculated the first moment of gray scale $E=\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{t}_a$ , second moment $\mathrm{\σ}={\left(\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{(t_a-E)}^2\right)}^{\frac{1}{2}}$ And third moment $s={\left(\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{(t_a-E)}^3\right)}^{\frac{1}{3}},$ Generate the training gray Moment Feature vector V of 3 dimensions ₂, wherein, t _arepresent the gray-scale value of a pixel of component block, the pixel number that N is component block;

(3b3) on each training square block, extract SURF feature,, centered by training square block center, the square window of a 20x20 of structure, is divided into 4x4 sub regions by this window, and every sub regions has 25 pixels; Each pixel calculated level to subregion and the little wave response of Haar of vertical direction, note is d respectively _xand d _y; By the response d of 25 pixels of subregion _x, d _yand absolute value | d _x|, | d _y| add up, every sub regions obtains 4 vectors: ∑ d _x, ∑ d _y, ∑ | d _x|, ∑ | d _y|, and then obtain the training SURF proper vector V that each square window generation 4x (4x4)=64 ties up ₃;

(3b4) on each training square block, extract LBP feature: the pixel value Yu Yikuai center by piece center is the center of circle, radius is that the pixel value of 8 points in 5 annular compares one by one, if center pixel value is larger than the pixel value of the upper point of annular, be 1 by the some assignment in this annular, otherwise be 0, and then with 18 bit of 8 dot generation on annular field; Just this 8 bit is converted to the decimal number of 256 again, generates the training LBP proper vector V of 256 dimensions ₄;

(3b5) by the training grey level histogram proper vector V of step (3b1)～(3b4) obtain ₁, training gray Moment Feature vector V ₂, training SURF proper vector V ₃, training LBP proper vector V ₄these four vectors are arranged in order in a column vector, obtain training feature vector V _f;

(3c) each parts training set U ⁱthe training feature vector V of all square blocks of each parts of every training portrait _fbe arranged in order in a column vector, obtain training component vector V _c, and then with each parts training set U ⁱthe training component vector V of 150 training portraits _ccomposition training component vector set

(3d) according to each training component vector set 150 affiliated portraits comprise five artists' paint, by training component vector set be divided into five groups of style collection, every group of style set pair answered an artist, and sets corresponding class mark for every group of style collection.

Step 4 generates support vector parameter between five groups of style collection and corresponding class mark.

The first input using five groups of style collection of step 3 as Support Vector Machine, and output using corresponding class mark as Support Vector Machine; Between input and output, generate again the support vector parameter of one group of member supporting vector parameter and five corresponding five groups of parts of parts.

Step 5, divides test set.

By pending portrait test set press the difference of parts, be divided into five test component collection respectively face, left eye, right eye, nose and nozzle component test set, represent that respectively the q in test set opens i parts of portrait:

(5a) by test set the former figure of every portrait as face's part, size is made as 176x239, by test set face's part of all portraits as face's part test set

(5b) with test set the pupil of left eye of every portrait centered by, the square chart picture that to get size be 30x22 is as left eye parts, with test set the left eye parts of all portraits as left eye unit test collection

(5c) with test set the pupil of right eye of every portrait centered by, the square chart picture that to get size be 30x22 is as right eye parts, with test set the right eye parts of all portraits as right eye unit test collection

(5d) with test set the center of two interpupillary lines of every portrait to centered by the intermediate point of nose, the square chart picture that to get size be 30x22 is as nose piece, with test set the nose piece of all portraits as nose piece test set

(5e) with test set the face center of every portrait centered by, the square chart picture that to get size be 30x22 is as nozzle component, with test set the nozzle component of all portraits as nozzle component test set

Step 6, at each unit test collection upper generation test component vector set

(6a) by each unit test collection in each parts be divided into test square block:

(6a1) by face's part test set face's part be divided into size for the test square block of 32x32, the square block that the lap between piece is 16x16;

(6a2) by left eye unit test collection left eye parts be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6a3) by right eye unit test collection right eye parts be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6a4) by nose piece test set nose piece be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6a5) by nozzle component test set nozzle component be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6b) on each test square block, generate testing feature vector

(6b1) on each test square block, extract grey level histogram feature, the pixel of the 0-255 of an each test square block gray level is counted, obtain a dimension and be 256 test grey level histogram proper vector the numerical value of every dimension is the pixel quantity of this gray level;

(6b2) on each test square block, extract gray Moment Feature, each test square block is calculated the first moment of gray scale $\tilde{E}=\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}\widetilde{t_r}$ , second moment $\widetilde{\mathrm{\σ}}={\left(\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}{(\widetilde{t_r}-\tilde{E})}^2\right)}^{\frac{1}{2}}$ And third moment $\tilde{s}={\left(\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}{(\widetilde{t_r}-\tilde{E})}^3\right)}^{\frac{1}{3}},$ Generate the test gray Moment Feature vector of 3 dimensions wherein, represent the gray-scale value of r pixel of component block, the pixel number that N is component block;

(6b3) on each test square block, extract SURF feature:, centered by test square block center, the square window of a 20x20 of structure, is divided into 4x4 sub regions by this window, and every sub regions has 25 pixels; Each pixel calculated level to subregion and the little wave response of Haar of vertical direction, note is done respectively with by the response of 25 pixels of subregion and absolute value add up, every sub regions obtains following 4 vectors: and then obtain each square window and generate the test SURF proper vector of 4x (4x4)=64 dimension

(6b4) on each test square block, extract LBP feature, pixel value Yu Yikuai center by piece center is the center of circle, radius is that the pixel value of 8 points in 5 annular compares one by one, if center pixel value is larger than the pixel value of the upper point of annular, be 1 by the some assignment in this annular, otherwise be 0, and then with 18 bit of 8 dot generation on annular field; Just this 8 bit is converted to the decimal number of 256 again, generates the test LBP proper vector of 256 dimensions

(6b5) by the test grey level histogram proper vector of step (6b1)～(6b4) obtain test gray Moment Feature vector test SURF proper vector test LBP proper vector these four vectors are arranged in order in a column vector, obtain testing feature vector

(6c) each unit test collection in the testing feature vector of all test square blocks of each parts be arranged in order in a column vector, obtain test component vector and then with each unit test collection the test component vectors of 50 test portraits composition test component vector set

Step 7, generates parts class mark.

With five test component vectors of every test portrait in step 6 with five the member supporting vector parameters of correspondence in step 4, as the input of Support Vector Machine, obtain five parts class marks.

Step 8, generates style class mark.

By five parts class marks of step 7, by the Voting principle of " the minority is subordinate to the majority ", obtain the style class mark of test portrait.

Effect of the present invention can be described further by following emulation experiment.

1. simulated conditions

The present invention is to be in Inter (R) Core (TM) i3-21003.10GHz, internal memory 4G, WINDOWS7 operating system at central processing unit, uses the MATLAB2010b of Mathworks company of U.S. exploitation to carry out emulation.Database adopts VIPSL database.SVM realizes the MATLAB kit " http://www.csie.ntu.edu.tw/～cjlin/libsvm/ " that adopts Taiwan Univ.'s woods intelligence benevolence.

2. emulation content

Get two groups of data in VIPSL database, every group has each 40 portraits of five artists, amounts to 200, and wherein 150 are made training portrait, and 50 are made test portrait.On VIPSL database, draw a portrait the classification of style by the inventive method, the classification results of two groups is as table 1.

Table 1. is used the portrait genre classification result based on Support Vector Machine on VIPSL database

As can be seen from Table 1, the portrait genre classification method based on Support Vector Machine of the present invention all has higher classification results in two groups of data.

Claims (9)

1. the portrait genre classification method based on Support Vector Machine, comprises the steps:

(1) partition database sample set: portrait collection is divided into training set U={U _p, p=1,2 ..., 150} and test set wherein U _prepresent that the p in training set opens portrait, represent that the q in test set opens portrait;

(2) divide training set: the difference that pending portrait training set U is pressed to parts, is divided into five training component collection respectively face, left eye, right eye, nose and nozzle component training set, represent that the p in training set opens i parts of portrait;

(3) at each parts training set U ⁱ, i=1,2 ... on 5, generate five groups of style collection and corresponding class mark;

(4) between five groups of style collection and corresponding class mark, generate support vector parameter: the first input using five groups of style collection of step (3) as Support Vector Machine, and output using corresponding class mark as Support Vector Machine; Between input and output, generate again the support vector parameter of one group of member supporting vector parameter and five corresponding five groups of parts of parts;

(5) divide test set: by pending portrait test set press the difference of parts, be divided into five test component collection respectively face, left eye, right eye, nose and nozzle component test set, represent that respectively test set q opens i parts of portrait;

(6) at each unit test collection upper generation test component vector set

(7) with every five test component vector sets that test is drawn a portrait in step (6) and five member supporting vector parameters of correspondence in step (4), as the input of Support Vector Machine, obtain five parts class marks;

(8), by five parts class marks of step (7), by the Voting principle of " the minority is subordinate to the majority ", obtain the style class mark of test portrait.

2. the method for the portrait genre classification based on Support Vector Machine according to claim 1, is characterized in that: step (2) described by pending portrait training set U by the difference of parts, be divided into five training component collection $U^i=\{U_p^i,i=\mathrm{1,2},...,5\},$ Carry out as follows:

(2a) using the former figure of every portrait of training set U as face's part, size is made as 176x239, using face's part of all portraits of training set U as the part training set U of face ¹;

(2b) centered by the pupil of left eye of every portrait of training set U, the square chart picture that to get size be 30x22 is as left eye parts, using the left eye parts of all portraits of training set U as left eye parts training set U ²;

(2c) centered by the pupil of right eye of every portrait of training set U, the square chart picture that to get size be 30x22 is as right eye parts, using the right eye parts of all portraits of training set U as right eye parts training set U ³;

(2d) by the center of two interpupillary lines of every portrait of training set U centered by the intermediate point of nose, the square chart picture that to get size be 30x22 is as nose piece, using the nose piece of all portraits of training set U as nose piece training set U ⁴;

(2e) centered by the face center of every portrait of training set U, the square chart picture that to get size be 30x22 is as nozzle component, using the nozzle component of all portraits of training set U as nozzle component training set U ⁵.

3. the method for the portrait genre classification based on Support Vector Machine according to claim 1, is characterized in that: step (3) described at each parts training set U ⁱfive groups of style collection of upper generation and corresponding class mark, carry out as follows:

(3a) by each parts training set U ⁱin each parts be divided into training square block:

(3b) on each training square block, generate training feature vector V _f:

(3c) each parts training set U ⁱthe training feature vector V of all training square block L of each parts of every portrait _fbe arranged in order in a column vector, obtain training component vector V _c, and then with each parts training set U ⁱthe training component vector V of 150 training portraits _ccomposition training component vector set

(3d) according to each training component vector set 150 affiliated portraits comprise five artists' paint, by training component vector set be divided into five groups of style collection, every group of style set pair answered an artist, and sets corresponding class mark for every group of style collection.

4. the method for the portrait genre classification based on Support Vector Machine according to claim 3, is characterized in that: step (3a) described by each parts training set U ⁱ, i=1,2 ..., the each parts in 5 are divided into training square block, carry out as follows:

(3a1) by the part training set U of face ¹face's part be divided into size for the training square block of 32x32, the square block that the lap between piece is 16x16;

(3a2) by left eye parts training set U ²left eye parts be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a3) by right eye parts training set U ³right eye parts be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a4) by nose piece training set U ⁴nose piece be divided into size for the training square block of 22x22, the square block that the lap between piece is 11x11;

(3a5) by nozzle component training set U ⁵nozzle component be divided into size for the training square block of 22x22, the lap size between piece is the square block of 11x11.

5. the method for the portrait genre classification based on Support Vector Machine according to claim 3, is characterized in that: what step (3b) was described generates training feature vector V on each training square block _f, carry out as follows:

(3b1) on each training square block, extract grey level histogram feature, the pixel of the 0-255 of an each training square block gray level is counted, obtain a dimension and be 256 training grey level histogram proper vector V ₁, the numerical value of every dimension is the pixel quantity of this gray level;

(3b2) on each training square block, extract gray Moment Feature, each training square block is calculated the first moment of gray scale $E=\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{t}_a$ , second moment $\mathrm{\σ}={\left(\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{(t_a-E)}^2\right)}^{\frac{1}{2}}$ And third moment $s={\left(\frac{1}{N}\underset{a=1}{\overset{N}{\mathrm{\Σ}}}{(t_a-E)}^3\right)}^{\frac{1}{3}},$ Generate the training gray Moment Feature vector V of 3 dimensions ₂, wherein, t _arepresent the gray-scale value of a pixel of component block, the pixel number that N is component block;

(3b3) on each training square block, extract SURF feature,, centered by training square block center, the square window of a 20x20 of structure, is divided into 4x4 sub regions by this window, and every sub regions has 25 pixels; Each pixel calculated level to subregion and the little wave response of Haar of vertical direction, note is d respectively _xand d _y; By the response d of 25 pixels of subregion _x, d _yand absolute value | d _x|, | d _y| add up, every sub regions obtains following 4 vectors: Σ d _x, Σ d _y, Σ | d _x|, Σ | d _y|, and then obtain the training SURF proper vector V that each square window generation 4x (4x4)=64 ties up ₃;

(3b4) on each training square block, extract LBP feature: the pixel value Yu Yikuai center by piece center is the center of circle, radius is that the pixel value of 8 points in 5 annular compares one by one, if center pixel value is larger than the pixel value of the upper point of annular, be 1 by the some assignment in this annular, otherwise be 0, and then with 18 bit of 8 dot generation on annular field; Just this 8 bit is converted to the decimal number of 256 again, generates the training LBP proper vector V of 256 dimensions ₄;

(3b5) by the training grey level histogram proper vector V of step (3b1)～(3b4) obtain ₁, training gray Moment Feature vector V ₂, training SURF proper vector V ₃, training LBP proper vector V ₄these four vectors are arranged in order in a column vector, obtain training feature vector V _f.

6. the method for the portrait genre classification based on Support Vector Machine according to claim 1, is characterized in that: step (5) described by pending portrait test set press the difference of parts, be divided into five test component collection ${\tilde{U}}^i=\{{\tilde{U}}_q^i,i=\mathrm{1,2},...,5\},$ Carry out as follows:

(5a) by test set the former figure of every portrait as face's part, size is made as 176x239, by test set face's part of all portraits as face's part test set

(5b) with test set the pupil of left eye of every portrait centered by, the square chart picture that to get size be 30x22 is as left eye parts, with test set the left eye parts of all portraits as left eye unit test collection

(5c) with test set the pupil of right eye of every portrait centered by, the square chart picture that to get size be 30x22 is as right eye parts, with test set the right eye parts of all portraits as right eye unit test collection

(5d) with test set the center of two interpupillary lines of every portrait to centered by the intermediate point of nose, the square chart picture that to get size be 30x22 is as nose piece, with test set the nose piece of all portraits as nose piece test set

(5e) with test set the face center of every portrait centered by, the square chart picture that to get size be 30x22 is as nozzle component, with test set the nozzle component of all portraits as nozzle component test set

7. the method for the portrait genre classification based on Support Vector Machine according to claim 1, is characterized in that: step (6) described at each unit test collection upper generation test component vector set carry out as follows:

(6a) by each unit test collection in each parts be divided into test square block;

(6b) on each test square block, generate testing feature vector

(6c) each unit test collection in the testing feature vector of all test square blocks of each parts be arranged in order in a column vector, obtain test component vector and then with each unit test collection the test component vectors of 50 test portraits composition test component vector set

8. the method for the portrait genre classification based on Support Vector Machine according to claim 7, is characterized in that: step (6a) is by each unit test collection in each parts be divided into test square block, carry out as follows:

(6a1) by face's part test set face's part be divided into size for the test square block of 32x32, the square block that the lap between piece is 16x16;

(6a2) by left eye unit test collection left eye parts be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6a3) by right eye unit test collection right eye parts be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11;

(6a4) by nose piece test set nose piece be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11

(6a5) by nozzle component test set nozzle component be divided into size for the test square block of 22x22, the square block that the lap between piece is 11x11.

9. the method for the portrait genre classification based on Support Vector Machine according to claim 7, is characterized in that: in described step (6b), on each test square block, generate testing feature vector carry out as follows:

(6b1) on each test square block, extract grey level histogram feature, the pixel of the 0-255 of an each test square block gray level is counted, obtain a dimension and be 256 test grey level histogram proper vector the numerical value of every dimension is the pixel quantity of this gray level;

(6b2) on each test square block, extract gray Moment Feature, each test square block is calculated the first moment of gray scale $\tilde{E}=\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}\widetilde{t_r}$ , second moment $\widetilde{\mathrm{\σ}}={\left(\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}{(\widetilde{t_r}-\tilde{E})}^2\right)}^{\frac{1}{2}}$ And third moment $\tilde{s}={\left(\frac{1}{N}\underset{r=1}{\overset{N}{\mathrm{\Σ}}}{(\widetilde{t_r}-\tilde{E})}^3\right)}^{\frac{1}{3}},$ Generate the test gray Moment Feature vector of 3 dimensions wherein, represent the gray-scale value of r pixel of component block, the pixel number that N is component block;

(6b3) on each test square block, extract SURF feature,, centered by test square block center, the square window of a 20x20 of structure, is divided into 4x4 sub regions by this window, and every sub regions has 25 pixels; Each pixel calculated level to subregion and the little wave response of Haar of vertical direction, note is done respectively with by the response of 25 pixels of subregion and absolute value add up, every sub regions obtains following 4 vectors: and then obtain each square window and generate the test SURF proper vector of 4x (4x4)=64 dimension

(6b4) on each test square block, extract LBP feature: the pixel value Yu Yikuai center by piece center is the center of circle, radius is that the pixel value of 8 points in 5 annular compares one by one, if center pixel value is larger than the pixel value of the upper point of annular, be 1 by the some assignment in this annular, otherwise be 0, and then with 18 bit of 8 dot generation on annular field; Just this 8 bit is converted to the decimal number of 256 again, generates the test LBP proper vector of 256 dimensions

(6b5) by the test grey level histogram proper vector of step (6b1)～(6b4) obtain test gray Moment Feature vector test SURF proper vector test LBP proper vector these four vectors are arranged in order in a column vector, obtain testing feature vector