CN105654141A - Isomap and SVM algorithm-based overlooked herded pig individual recognition method - Google Patents

Abstract

The invention discloses an Isomap and SVM algorithm-based overlooked herded pig individual recognition method and belongs to the field of machine vision and mode recognition. According to the method, firstly, the foreground detection and the target extraction are conducted for the acquired video frames of overlooked herded pigs, and then individual pigs can be obtained. Secondly, a training sample is established, and the color feature, the texture feature and the shape feature of individual pigs are extracted and combined to form feature vectors that represent individual pigs. Thirdly, the feature fusion is conducted on the above combined features based on the Isomap algorithm. On the premise that the effective recognition information is maintained maximally, the feature dimension is lowered. Finally, the training and the recognizing are conducted by a kernel function-optimized support vector machine classifier. The above method provides a novel idea for the recognition of non-excitable individual pigs, and also lays a foundation for the further research on the behavior analysis of individual pigs.

Description

Vertical view group support pig individual discrimination method based on Isomap and SVM algorithm

Technical field

The present invention relates to machine vision technique and mode identification technology, be specifically related under a kind of vertical view state pig individual discrimination method in group support pig monitor video.

Background technology

Development along with scale pig industry and computer technology, inquire into the foreground detection overlooked in group support pig video sequence based on machine vision, pig individual behavior analysis etc. has increasingly been subject to the concern (GuoYizheng of Chinese scholars, ZhuWeixing, JiaoPengpeng, etal.Multi-objectextractionfromtopviewgroup-housedpigima gesbasedonadaptivepartitioningandmultilevelthresholdings egmentation [J] .Biosystemsengineering, 2015, 135 (5): 54-60.). the problem that wherein difficulty is maximum and crucial is the identification that pig is individual in motor process, now widely used method is ear tag RFID (MaselyneJ, SaeysW, NuffelAV.Review:quantifyinganimalfeedingbehaviourwithafo cusonpigs [J] .Physiology&Behavior, 2015, 138:37-51.), Kashiha etc. (2014) are in order to identify that in group support pig, pig is individual, back in pig coats the labelling of different colours and shape, individual (the KashihaMA of pig is identified by methods such as ellipse fittings, BahrC, OttS, etal.Automaticidentificationofmarkedpigsinapenusingimage patternrecognition [J] .ComputersandElectronicsinAgriculture, 2013, 93 (2): 111-120.). in addition, machine vision technique is utilized also not have pertinent literature to report the research of scale pig farm group support pig individual identification.

Consider machine vision algorithm successful Application (such as recognition of face, iris identification, fingerprint recognition, Gait Recognition etc.) in the identification of people, inquire into the pig individual identification based on machine vision and be possibly realized.

Summary of the invention

It is an object of the invention to pig individuality in group support pig monitor video under vertical view state is identified, propose a kind of based on Isomap (IsometricMapping for this, Isometric Maps) and the vertical view group support pig individual discrimination method of SVM (supportvectormachine, support vector machine) algorithm.

The technical solution used in the present invention is: the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm comprises the following steps:

Step 1, obtains group support pig video sequence under vertical view state, then does Image semantic classification, and mainly group support pig foreground detection is extracted with pig individual goal;Step 2, pig personal feature is extracted, and including using color moment method to extract the color characteristic that pig is individual, uses co-occurrence matrix method to extract the textural characteristics that pig is individual, using not displacement method to extract the shape facility that pig is individual, can effectively describe, thus establishing, the characteristic parameter that pig is individual; Step 3, after respectively the color characteristic in step 2, textural characteristics and shape facility being standardized, adopts Isomap algorithm to carry out merging optimization; Step 4, utilizes the support vector machine classifier optimizing kernel function that sample is trained, and each pig individuality treating knowledge is identified.

Further, described step 1 specifically includes:

First reconstruction experiment pig house, installs shooting directly over pig house and overlooks the image capturing system of video, obtain and overlook group support pig color video fragment; Then qualified frame of video is chosen, single-frame images is done histogram equalization and the segmentation of maximum entropy threshold value, segmentation result doing mathematics Morphological scale-space to pig zone of action further, is only comprised the image of foreground target, and then is extracted each pig individual goal.

Further, the described concrete grammar to the segmentation result doing mathematics Morphological scale-space of pig zone of action is:

Step 1.1, result is done opening operation by the disc structure element utilizing radius fixing, removes the isolated noise point in result, disconnects the adhesion between target and between target and background, keeps target life size to be basically unchanged;

Step 1.2, labelling connected region, target is removed less than the region of certain number of pixels, because according to pig individual characteristic, being foreground target region scarcely less than the connected region of certain area;

Step 1.3, fills bianry image, i.e. non-prospect cavity within filling part pig individuality.

Further, the detailed process of described step 2 is:

Step 2.1, based on the pig individuality color feature extracted of color moment: owing to colouring information is concentrated mainly in low-order moment, therefore only need to extract single order, second order and third moment to each color passage and add up; Each pig individuality is extracted R, B, G 9 color characteristics of tri-passages, is designated as [��_R��_RS_R��_G��_GS_G��_B��_BS_B];

Step 2.2, based on the pig individuality texture feature extraction of co-occurrence matrix: co-occurrence matrix is described as be on �� direction, a pair pixel of standoff distance d is respectively provided with gray value i and the j probability occurred, is designated as p (i, j; D, ��); Selecting angle second moment, contrast, unfavourable balance to divide square, entropy these four features having descriptive power most respectively, each pig individuality is extracted 4 textural characteristics, is designated as [ASMCONIDMENT];

Step 2.3, based on the pig individual shapes feature extraction of not bending moment: (x, y), the geometric moment definition on its p+q rank is the discrete picture f of a width M �� NWherein, i �� M, j �� N, p, q are constant; OrderR=(p+q+2)/2, can derive 7 translations, convergent-divergent, rotationally-varying constant absolute inequality, can extract 7 invariant moment features shape facility as image accordingly; So, every pig individuality is extracted 7 shape facilities, is designated as [M₁M₂M₃M₄M₅M₆M₇]��

Further, described step 3 detailed process is:

Step 3.1, in the characteristic extraction procedure that pig is individual, first color characteristic, textural characteristics and shape facility are standardized, between proper normalization to [-1+1], color characteristic, textural characteristics and shape facility combine and build the characteristic vector characterizing pig individuality;

Step 3.2, adopts Isomap algorithm to carry out Feature Fusion optimization; Isomap algorithm main process is as follows:

Step 3.2.1, it is determined that neighborhood: determine each neighborhood of a point with k nearest neighbour method in theorem in Euclid space, and calculate the distance between each point and its all neighborhood point;

Step 3.2.2, calculates shortest distance matrix: obtains neighborhood distance afterwards by step 3.2.1, then calculates any two points shortest path in Neighborhood Graph, is write the result obtained as matrix form, be designated as D_N��N;

Step 3.2.3, the low-dimensional solving manifold with multi-dimentional scale conversion MDS represents Y: set H_N��N=-JDJ/2, wherein J=I-ee^T/ N, e=(1 ..., 1)^TFor N dimensional vector, I is that N ties up unit matrix. Solve maximum f the eigenvalue �� of H₁..., ��_fAnd characteristic of correspondence vector u₁..., u_f, and remember U=[u₁..., u_f], then

Further, in the support vector machine classifier of described step 4, the kernel function of support vector machine is to combine radial direction base RBF kernel function and Sigmoid kernel function to be constructed as follows mixed kernel function:

K (x, y)=(1-��) K_sigmoid(x, y)+�� K_rbf(x, y)

=(1-��) tanh (v (x_i��x_j)-r)+��exp(-��||x-y||²)

Wherein �� �� (0,1), for the weights of kernel function; Parameter C and the �� of mixed kernel function can use particle swarm optimization algorithm to obtain; Each pig individuality finally treating knowledge is identified.

The invention has the beneficial effects as follows:

Traditional Man is observed and is identified that the mode of pig individuality wastes time and energy, and affects the health of staff. Although ear tag RFID mode is without artificial Real Time Observation, but also can disturb pig normal growth to a certain extent. Proposed here a kind of method pig individuality stress not being identified by machine vision technique. Utilize Isomap algorithm that color, texture and shape three category feature are merged, both can remove the redundancy between feature, retain identification information, it is also possible to reduce intrinsic dimensionality, reduce classified counting amount. SVM is utilized can effectively to process multiclass identification problem, the mixed kernel function proposed both had considered global property, have also contemplated that local characteristics, namely the learning capacity of SVM and generalization ability are obtained for reinforcement, and the more single kernel function SVM of SVM based on mixed kernel function is more suitable for overlooking the individual identification of the many pigs of group support. Isomap algorithm and mixed kernel function SVM are combined, has given full play to respective advantage, efficiently solve non-rigid under complex background and overlook the identification problem of group support pig. This pig individuality stress not be identified by machine vision technique, both the treatment that pig is provided sufficient by poultry raiser it had been easy to, improve the welfare of pig, also provide technical foundation for later stage pig individual behavior analysis, to improving breeding scale industry automatization and Intellectualized monitoring level offer technical support.

Accompanying drawing explanation

Below in conjunction with the drawings and specific embodiments, the present invention is described in further details:

Fig. 1 is pig individual identification flow chart.

Fig. 2 is certain two field picture foreground detection result example.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described.

Fig. 1 is pig individual identification flow chart, and wherein Image semantic classification purpose is foreground detection, obtains each pig individual goal; Feature extraction comprises the color feature extracted based on color moment, based on the texture feature extraction of gray level co-occurrence matrixes, based on the Shape Feature Extraction of not bending moment; What Feature Fusion optimization adopted is Isomap algorithm; Model of cognition adopts the support vector machine classifier optimizing kernel function; May finally realize based on machine vision automatically stress not each pig individuality of identification.

Step 1: obtain group support pig video sequence under vertical view state, then do Image semantic classification, mainly group support pig foreground detection is extracted with pig individual goal.

(1) reconstruction pig house, obtains group support pig video sequence under vertical view state.

Concrete grammar is to install shooting directly over pig house to overlook the image capturing system of video, obtains and overlooks group support pig color video fragment.

(2) histogram equalization of image and the segmentation of maximum entropy threshold value.

Choose qualified frame of video, single-frame images is done histogram equalization and the segmentation of maximum entropy threshold value, the segmentation result doing mathematics Morphological scale-space to pig zone of action further, is only comprised the image of foreground target, and then extract each pig individual goal.

Wherein morphology processing specifically includes: result is done "ON" computing by the disc structure element 1) utilizing radius fixing, remove the isolated noise point in result, disconnect between target and adhesion between target and background, target life size can be kept again to be basically unchanged; 2) labelling connected region, removes target less than the region of certain number of pixels, because according to pig individual characteristic, being foreground target region scarcely less than the connected region of certain area (in region, sum of all pixels is less than 18000); 3) bianry image, i.e. non-prospect " cavity " within filling part pig individuality are filled. Fig. 2 is certain two field picture foreground detection result example, and then can extract each pig individual goal.

Step 2: pig personal feature is extracted, the color characteristic that pig is individual is extracted including using color moment method, use co-occurrence matrix method to extract the textural characteristics that pig is individual, using not displacement method to extract the shape facility that pig is individual, can effectively describe, thus establishing, the characteristic parameter that pig is individual.

(1) based on the pig individuality color feature extracted of color moment

Owing to colouring information is concentrated mainly in low-order moment, therefore only each color passage need to be extracted single order, second order and third moment and add up. If h_ijRepresenting that in i-th Color Channel component, gray scale is the probability of the pixel appearance of j, n is total number-of-pixels, then three low-order moment mathematic(al) representations of color moment are:

${\mathrm{\μ}}_i=\frac{1}{n}{\Σ}_{j=1}^n=h_{ij}---\left(1\right)$

${\mathrm{\σ}}_i={\left(\frac{1}{n}{\Σ}_{j=1}^n{\left(h_{ij}-{\mathrm{\μ}}_i\right)}^2\right)}^{\frac{1}{2}}---\left(2\right)$

$S_i={\left(\frac{1}{n}{\Σ}_{j=1}^n{\left(h_{ij}-{\mathrm{\μ}}_i\right)}^3\right)}^{\frac{1}{3}}---\left(3\right)$

These 3 low-order moment are called average, variance and degree of skewness.

(2) based on the pig individuality texture feature extraction of co-occurrence matrix

Co-occurrence matrix is described as be on �� direction, and a pair pixel of standoff distance d is respectively provided with gray value i and the j probability occurred, is designated as p (i, j; D, ��). Redundancy is there is between 14 features of gray level co-occurrence matrixes in that considers that Haralick proposes for analyzing, here comprehensively having selected angle second moment ASM (AngularSecondMoment), contrast C ON (Contrast), unfavourable balance to divide 4 features having descriptive power most such as square IDM (InverseDifferenceMoment), entropy ENT (Entropy), its definition is as follows respectively:

$ASM=\underset{i=0}{\overset{L-1}{\Σ}}\underset{j=0}{\overset{L-1}{\Σ}}{\[p(i,j;d,\mathrm{\θ})\]}^2---\left(4\right)$

$CON=\underset{i=0}{\overset{L-1}{\Σ}}\underset{j=0}{\overset{L-1}{\Σ}}{(i-j)}^{2}p(i,j;d,\mathrm{\θ})---\left(5\right)$

$IDM=\underset{i=0}{\overset{L-1}{\Σ}}\underset{j=0}{\overset{L-1}{\Σ}}\[\frac{p(i,j;d,\mathrm{\θ})}{1+{(i-j)}^2}\]---\left(6\right)$

$ENT=\underset{i=0}{\overset{L-1}{\Σ}}\underset{j=0}{\overset{L-1}{\Σ}}p(i,j;d,\mathrm{\θ})\lg p(i,j;d,\mathrm{\θ})---\left(7\right)$

(3) based on the pig individual shapes feature extraction of not bending moment

(x, y), the geometric moment definition on its p+q rank is the discrete picture f of one width M �� NWherein, i �� M, j �� N, p, q are constant. OrderR=(p+q+2)/2, can derive 7 displacements, convergent-divergent, rotationally-varying constant absolute inequality, can extract 7 invariant moment features shape facility as image accordingly. As follows respectively:

M₁=��₂₀+��₀₂(8)

$M_2={({\mathrm{\μ}}_{20}-{\mathrm{\μ}}_{02})}^2+4{\mathrm{\μ}}_{11}^2---\left(9\right)$

M₃=(��₃₀-3��₁₂)²+(3��₂₁-��₀₃)²(10)

M₄=(��₃₀+��₁₂)²+(��₂₁+��₀₃)²(11)

M₅=(��₃₀-3��₁₂)��₃₀+��₁₂)[(��₃₀+��₁₂)²-3(��₂₁+��₀₃)²]+(3��₂₁-��₀₃)(��₂₁+��₀₃)[3(��₃₀+��₁₂)²-(��₂₁+��₀₃)²](12)

M₆=(��₂₀-3��₀₂)[(��₃₀+��₁₂)²-(��₂₁+��₀₃)²]+4��₁₁(��₃₀+��₁₂)²(��₂₁+��₀₃)(13)

M₇=(3 ��₁₂-��₀₃)(��₃₀+��₁₂)[(��₃₀+��₁₂)²-3(��₂₁+��₀₃)²]+(��₃₀-3��₁₂)(��₂₁+��₀₃)[3(��₃₀+��₁₂)²-(��₂₁+��₀₃)²](14)

To sum up, each pig individuality is extracted R, B, G 9 color characteristics of tri-passages; It is extracted 4 textural characteristics based on gray level co-occurrence matrixes; It is extracted 7 shape facilities based on not bending moment, collectively forms and characterize the characteristic parameter that pig is individual.

Step 3: after respectively the color in step 2, texture and shape facility being standardized, adopts Isomap algorithm to carry out merging optimization.

First above-mentioned color, texture and shape facility are standardized, between proper normalization to [-1+1], combined to build by color characteristic, textural characteristics and shape facility and characterize the characteristic vector that pig is individual, affect classifying quality because magnitude range differs eliminating numerical value.

Then Isomap algorithm is adopted to carry out Feature Fusion optimization. Algorithm main process is as follows:

1) neighborhood is determined. Theorem in Euclid space determines each neighborhood of a point with k nearest neighbour method, and calculates the distance between each point and its all neighborhood point.

2) shortest distance matrix is calculated. By step 1) obtain neighborhood distance afterwards, then calculate any two points shortest path in Neighborhood Graph, write the result obtained as matrix form, be designated as D_N��N��

3) convert MDS with multi-dimentional scale to solve the low-dimensional of manifold and represent Y. If H_N��N=-JDJ/2, wherein J=I-ee^T/ N, e=(1 ..., 1)^TFor N dimensional vector, I is that N ties up unit matrix. Solve maximum f the eigenvalue �� of H₁..., ��_fAnd characteristic of correspondence vector u₁..., u_f, and remember U=[u₁..., u_f], then

Step 4: utilizing the support vector machine classifier optimizing kernel function that sample is trained, each pig individuality treating knowledge is identified.

It is provided with classified sample setWherein sampling feature vectorsNamelyBeing the G vector tieing up in real number space, (-1 ,+1}, S is classification samples number to class label y ��. Build a classification function by these samples, make it test data are correctly classified as far as possible. Classification seeks a hyperplane exactlyMake it completely separable for two class samples, whereinFor planar process vector, b is bias term.

Realistic problem is nearly all Nonlinear separability, and uses kernel function that the nonlinear input space can be mapped to the space of a linear separability. Now optimization object function is:

$L\left(\mathrm{\α}\right)=\underset{i=1}{\overset{S}{\Σ}}{\mathrm{\α}}_i-\frac{1}{2}\underset{i=1}{\overset{S}{\Σ}}\underset{j=1}{\overset{S}{\Σ}}{\mathrm{\α}}_i\·{\mathrm{\α}}_j\·y_i\·y_j\·K(x_i,y_j)---\left(15\right)$

$\begin{array}{cccc}s.t.& \underset{i=1}{\overset{S}{\Σ}}{\mathrm{\α}}_i\·y_i=0,& 0\≤{\mathrm{\α}}_i\≤C& \∀i\end{array}---\left(16\right)$

To new sample, classification function is:

$h\left(x\right)=\mathrm{sgn}(\underset{i=1}{\overset{S}{\Σ}}{\mathrm{\α}}_i^{*}\·y_i\·K(x_i,x_j)+b^{*})---\left(17\right)$

Wherein �� is Lagrange factor, and K is kernel function, and C is penalty factor.

Kernel function support vector machine has become as a kind of instrument that the problem aspects such as classification and recurrence are popular and the most powerful that solves at present. Different kernel functions will produce different classifying qualities, two class kernel functions is combined here, is constructed as follows mixed kernel function:

K (x, y)=(1-��) K_sigmoid(x, y)+�� K_rbf(x, y) (18)

=(1-��) tanh (v (x_i��x_j)-r)+��exp(-��||x-y||²)

Wherein �� �� (0,1), for the weights of kernel function. Parameter C and the �� of mixed kernel function can use particle swarm optimization algorithm to obtain.

Preferred embodiment:

One optimum detailed description of the invention of the present invention: reconstruction pig house, under acquisition vertical view state in group support pig video sequence process, 3m place directly over pig house (long * width * height is 3.5m*3m*1m), shooting is installed and overlooks the image capturing system of video, obtain hutch number be 7 to 12, different growing stages, the color video fragment that comprises complex scene, image resolution ratio is 1760 �� 1840 pixels. In pig personal feature extraction process, each pig individuality is extracted R, B, G 9 color characteristics of tri-passages, is designated as [��_R��_RS_R��_G��_GS_G��_B��_BS_B], so can obtain the pig the most direct visual signature of individuality; Being extracted 4 textural characteristics based on gray level co-occurrence matrixes, be designated as [ASMCONIDMENT], 4 textural characteristics can fully describe the key character of pig individuality surface texturisation structure; It is extracted 7 shape facilities based on not bending moment, is designated as [M₁M₂M₃M₄M₅M₆M₇], these 7 shape facilities effect in differentiation pig individual shapes is just more apparent, and three category features together constitute and characterize the characteristic parameter that pig is individual. Neighborhood k value 4 in Isomap algorithm, calculates 2 shortest paths in Neighborhood Graph and uses dijkstra's algorithm, and optimum dimension is 5. Mixed kernel function SVM classifier major parameter is as follows: v takes 1/5, r and takes 3, C and take 16, �� and take 1.82, �� and take 0.8.Through above-mentioned steps, finally achieve overlooking the identification that in group support pig image sequence, pig is individual.

In sum, a kind of of the present invention utilizes machine vision technique to overlooking the method that in group support pig image sequence, pig individuality is identified, and first the vertical view group support pig frame of video gathered is carried out foreground detection and Objective extraction, it is thus achieved that single pig individuality; Thereafter setting up training sample, extract pig individuality color, texture and shape facility, combination builds and characterizes the characteristic vector that pig is individual; Assemblage characteristic then utilizes Isomap algorithm do Feature Fusion, at utmost retaining, the basis effectively identifying information reduces intrinsic dimensionality; The support vector machine classifier optimizing kernel function is finally utilized to be trained and identification. The research be stress not pig individual identification provide new approaches, also lay a good foundation for exploring further group support pig individual behavior analysis etc.

In the description of this specification, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " illustrative examples ", " example ", " concrete example " or " some examples " etc. means in conjunction with this embodiment or example describe are contained at least one embodiment or the example of the present invention. In this manual, the schematic representation of above-mentioned term is not necessarily referring to identical embodiment or example. And, the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiments or example.

Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: these embodiments can being carried out multiple change, amendment, replacement and modification when without departing from principles of the invention and objective, the scope of the present invention is limited by claim and equivalent thereof.

Claims (6)

1. based on the vertical view group support pig individual discrimination method of Isomap and SVM algorithm, it is characterised in that comprise the following steps:

Step 1, obtains group support pig video sequence under vertical view state, then does Image semantic classification, and mainly group support pig foreground detection is extracted with pig individual goal; Step 2, pig personal feature is extracted, and including using color moment method to extract the color characteristic that pig is individual, uses co-occurrence matrix method to extract the textural characteristics that pig is individual, using not displacement method to extract the shape facility that pig is individual, can effectively describe, thus establishing, the characteristic parameter that pig is individual; Step 3, after respectively the color characteristic in step 2, textural characteristics and shape facility being standardized, adopts Isomap algorithm to carry out merging optimization; Step 4, utilizes the support vector machine classifier optimizing kernel function that sample is trained, and each pig individuality treating knowledge is identified.

2. the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm according to claim 1, it is characterised in that: described step 1 specifically includes:

First reconstruction experiment pig house, installs shooting directly over pig house and overlooks the image capturing system of video, obtain and overlook group support pig color video fragment; Then qualified frame of video is chosen, single-frame images is done histogram equalization and maximum entropy threshold segmentation, segmentation result doing mathematics Morphological scale-space to pig zone of action further, is only comprised the image of foreground target, and then is extracted each pig individual goal.

3. the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm according to claim 2, it is characterised in that: the described concrete grammar to the segmentation result doing mathematics Morphological scale-space of pig zone of action is:

Step 1.1, result is done opening operation by the disc structure element utilizing radius fixing, removes the isolated noise point in result, disconnects the adhesion between target and between target and background, keeps target life size to be basically unchanged;

Step 1.2, labelling connected region, target is removed less than the region of certain number of pixels, because according to pig individual characteristic, being foreground target region scarcely less than the connected region of certain area;

Step 1.3, fills bianry image, i.e. non-prospect cavity within filling part pig individuality.

4. the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm according to claim 1, it is characterised in that: the detailed process of described step 2 is:

Step 2.1, based on the pig individuality color feature extracted of color moment: owing to colouring information is concentrated mainly in low-order moment, therefore only need to extract single order, second order and third moment to each color passage and add up; Each pig individuality is extracted R, B, G 9 color characteristics of tri-passages, is designated as [��_R��_RS_R��_G��_GS_G��_R��_RS_B];

Step 2.2, based on the pig individuality texture feature extraction of co-occurrence matrix: co-occurrence matrix is described as be on �� direction, a pair pixel of standoff distance d is respectively provided with gray value i and the j probability occurred, is designated as p (i, j; D, ��); Selecting angle second moment, contrast, unfavourable balance to divide square, entropy these four features having descriptive power most respectively, each pig individuality is extracted 4 textural characteristics, is designated as [ASMCONIDMENT];

Step 2.3, based on the pig individual shapes feature extraction of not bending moment: (x, y), the geometric moment definition on its p+q rank is the discrete picture f of a width M �� NWherein, i �� M, j �� N, p, q are constant; OrderR=(p+q+2)/2, can derive 7 translations, convergent-divergent, rotationally-varying constant absolute inequality, can extract 7 invariant moment features shape facility as image accordingly; So, every pig individuality is extracted 7 shape facilities, is designated as [M₁M₂M₃M₄M₅M₆M₇]��

5. the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm according to claim 1, it is characterised in that: the detailed process of described step 3 is:

Step 3.1, in the characteristic extraction procedure that pig is individual, first color characteristic, textural characteristics and shape facility are standardized, between proper normalization to [-1+1], color characteristic, textural characteristics and shape facility combine and build the characteristic vector characterizing pig individuality;

Step 3.2, adopts Isomap algorithm to carry out Feature Fusion optimization; Isomap algorithm main process is as follows:

Step 3.2.1, it is determined that neighborhood: determine each neighborhood of a point with k nearest neighbour method in theorem in Euclid space, and calculate the distance between each point and its all neighborhood point;

Step 3.2.2, calculates shortest distance matrix: obtains neighborhood distance afterwards by step 3.2.1, then calculates any two points shortest path in Neighborhood Graph, is write the result obtained as matrix form, be designated as D_N��N;

Step 3.2.3, the low-dimensional solving manifold with multi-dimentional scale conversion MDS represents Y: set H_N��N=-JDJ/2, wherein J=I-ee^T/ N, e=(1 ..., 1)^TFor N dimensional vector, I is that N ties up unit matrix, solves maximum f the eigenvalue �� of H₁..., ��_fAnd characteristic of correspondence vector u₁..., u_f, and remember U=[u₁..., u_f], then

6. the vertical view group support pig individual discrimination method based on Isomap and SVM algorithm according to claim 1, it is characterized in that: in the support vector machine classifier of described step 4, the kernel function of support vector machine is to combine radial direction base RBF kernel function and Sigmoid kernel function to be constructed as follows mixed kernel function:

K (x, y)=(1-��) K_sigmoid(x, y)+�� K_rbf(x, y)

=(1-��) tanh (�� (x_i��x_j)-r)+��exp(-��||x-y||²)

Wherein �� �� (0,1), for the weights of kernel function;Parameter C and the �� of mixed kernel function can use particle swarm optimization algorithm to obtain; Each pig individuality finally treating knowledge is identified.