CN106203528B - It is a kind of that intelligent classification algorithm is drawn based on the 3D of Fusion Features and KNN - Google Patents
It is a kind of that intelligent classification algorithm is drawn based on the 3D of Fusion Features and KNN Download PDFInfo
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Abstract
It is a kind of that intelligent classification algorithm is drawn based on the 3D of Fusion Features and KNN, include the following steps: that Lbp algorithm, Gist algorithm and the progress 3D picture piece feature extraction of Phog algorithm, which 1) is respectively adopted, obtains corresponding feature vector, 2) feature vector extracted is merged, obtains fusion feature；3) it is based on fusion feature, Intelligence Classifier is drawn using KNN algorithm building 3D.The present invention is a kind of innovative automatic mobile phone storage attempted, 3D can be promoted to draw for combining computer with art, provides for designer and masses and enriches comprehensive 3D and draw browsing and retrieve.In algorithm design aspect, LBP feature can extract the texture in image, Gist feature can extract image space envelope, PHOG feature can extract topography edge, the fusion of these features can comprehensively reflect mural painting, draw, draw to wall and recessed corner draw art difference, help to improve 3D draw classification accuracy.
Description
Technical field
The present invention relates to 3D to draw classification field, especially a kind of to draw intelligent classification algorithm based on the 3D of Fusion Features and KNN.
Background technique
In recent years, naked eye 3D is drawn with its special artistic expression, the interaction of superpower vision impact and great entertaining
Property receive more and more attention and pursue, cover advertisement, exhibition, the multiple fields such as household, have vast potential for future development.
Under normal circumstances, 3D draw be classified according to its artistic expression, such as mural painting, draw, draw to wall, recessed corner is drawn
Deng.However, more and more works are created next by artists as 3D draws the popular development and business application of art.Such as
What, which is effectively collected, summarizes a large amount of 3D picture, and building 3D draws database, carries out accurate and quick management and retrieval 3D and draws, Cheng Liao
3D draws design and the urgent need of application industry.The service that this vertical field is drawn for 3D, there has been no enterprises and tissue to carry out
Related work.
Summary of the invention
It is a kind of based on the 3D of Fusion Features and KNN picture intelligent method for classifying it is a primary object of the present invention to propose, it can be right
The picture that user uploads carries out intelligent recognition and classification based on artistic expression, facilitates the automated storing system of 3D picture
Building.
The present invention adopts the following technical scheme:
It is a kind of based on the 3D of Fusion Features and KNN draw intelligent classification algorithm, it is characterised in that: 1) be respectively adopted Lbp algorithm,
Gist algorithm and Phog algorithm carry out the piece feature extraction of 3D picture and obtain corresponding feature vector, 2) feature vector that will be extracted
It is merged, obtains fusion feature；3) it is based on fusion feature, Intelligence Classifier is drawn using KNN algorithm building 3D.
Preferably, in step 1), the 3D that a Zhang great little is M × N is drawn and is calculated as img by invariable rotary mode LPB
Method extracts several key feature points, generates feature vector FVLBP。
Preferably, the LPB algorithm is specific as follows
1.1A) using R as the P vertex neighborhood of radius, gcCentered on, gpFor neighborhood point, it is bigger than center brightness or small to distinguish neighborhood
Method beS (x)=1 (x>=0) in formula, s (x)=0 (x<0)；
It 1.2A) is concentrated in the point of annular neighbours, if the position of center pixel is (x, y), vicinity points giPosition
It calculates are as follows:
1.3A) coding on annular neighbours' collection is carried out to operate ROR by right ring shift right, obtains LBP invariable rotary coding,
Value is the smallest to be encoded to last LBP coding.Rotation process is as follows:
1.4A) by estimate U by LBP encode in the codings of U≤2 be classified as equivalent formulations class, in addition to equivalent formulations class
Mode be all classified as another kind of, referred to as mixed mode class, estimate U and be defined as
1.5A) by rotation and normalization, the LBP feature of a width picture is comprising P+2 characteristic point.
Preferably, in step 1), the 3D that a width size is M × N is drawn, several is extracted by Gist algorithm as img
Key feature points generate feature vector FVGist。
5, as claimed in claim 4 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, feature exists
In: the Gist algorithm is specific as follows
1.1B) by 3D figure phase gray processing and it is scaled to 128*128；
The gray level image f (x, y) that a width size is r × c 1.2B) is divided into np*npThe grid of specification, then grid block number
For ng=np*np, each grid block by row be successively denoted as Pi, wherein i=1,2 ..., ng；The size of grid block is r ' × c ',
Middle r '=r/np, c '=c/np；
N 1.3B) is used respectivelycThe filter in a channel carries out convolutional filtering to image, then each channel filtering of each grid block
Cascade result becomes block Gist (PG) feature afterwards, i.e.,
Wherein (x, y) ∈ Pi, gmn(x, y)=α-mG (x ', y '), α > 1 are multiple dimensioned multi-direction Gabor filters, x '=
α-m(xcos θ+ycos θ), y '=α-m(- xsin θ+ycos θ), θ=n π/(n+1),X in formula, y are image pixel coordinates position,
σx,σyIt is the variance of the Gauss factor on the direction x and y, f respectively0It is filter centre frequency, φ is the phase difference of the harmonic factor,
α-mFor the scale factor of morther wavelet expansion, θ is rotation angle, and m is scale parameter, and n is direction number, GPDimension be nc×r′×
c′；
1.4B) to GPBy the combined result of the row referred to as overall situation Gist (GG) feature after mean value, i.e., each channel filtering result takesWhereinGGDimension be nc×ng。
Preferably, in step 1), the 3D that a width size is M × N is drawn, several is extracted by PHOG algorithm as img
Key feature points generate feature vector FVPHOG。
Preferably, the PHOG algorithm is specific as follows
1.1C) selection first layer divides, and is divided into the small cells of 1*1；
The gradient of each pixel in each cell 1.2C) is calculated, i.e.,I in formulax, IyRepresent the ladder on both horizontally and vertically
Angle value, M (x, y), θ (x, y) respectively indicate range value and the direction of gradient；
8 bin 1.3C) are divided by 360 degree, each bin includes 45 degree, and entire histogram includes 8 dimensions, then basis
Its amplitude is added in histogram using bilinear interpolation value method, obtains whole picture figure at this time by the gradient direction of each pixel
Small HOG feature be 1*8=8；
1.4C) the selection second layer divides, and is divided into the small cells of 2*2, returns to 1.2C), until the small HOG feature of whole picture figure
For 4*8=32, into 1.5C)；
1.5C) selection third layer divides, and is divided into the small cells of 4*4, returns to 1.2C), until the small HOG feature of whole picture figure
For 16*8=128, into 1.6)；
The 4th layer of division 1.6C) is selected, the small cells of 8*8 is divided into, returns to 1.2), until the small HOG feature of whole picture figure is
64*8=512, into 1.7)；
It is cascaded after 1.7C) four layers small HOG feature is normalized, then obtaining total characteristic is 8+32+128+512=680.
Preferably, data set is drawn to 3D using KNN classifier based on fusion feature in step 3) and carries out algorithm experimental, tool
Body are as follows: data set include mural painting, draw, draw to wall and recessed corner；Each classification one half-sample of random selection as training dataset,
Remaining sample is repeated 3 times as test set data, the random sampling test, is averaged as report result；It was training
Cheng Zhong, distance metric are Euclidean distance.
By the above-mentioned description of this invention it is found that compared with prior art, the invention has the following beneficial effects:
The present invention is a kind of innovative trial for combining computer with art, and the automatic mobile phone that 3D can be promoted to draw is deposited
Storage provides for designer and masses and enriches comprehensive 3D picture browsing and retrieval.In algorithm design aspect, LBP feature can be extracted
Texture in image, Gist feature can extract image space envelope, and PHOG feature can extract topography edge, these features
Fusion can comprehensively reflect mural painting, draw, draw to wall and recessed corner draw art difference, help to improve 3D draw classification standard
Exactness.
Detailed description of the invention
Fig. 1 is classification results schematic diagram of the present invention.
Specific embodiment
Below by way of specific embodiment, the invention will be further described.
It is a kind of that intelligent classification algorithm is drawn based on the 3D of Fusion Features and KNN, include the following steps
1) Lbp algorithm, Gist algorithm and the progress 3D picture piece feature extraction of Phog algorithm is respectively adopted and obtains corresponding spy
Vector is levied, specific as follows:
1A) 3D that a Zhang great little is M × N is drawn crucial special by the extraction of invariable rotary mode LPB algorithm 18 as img
Point is levied, feature vector FV is generatedLBP, it is denoted as FVLBP=(x1, x2..., x18), it is specific as follows:
1.1A) using R as the P vertex neighborhood of radius, gcCentered on, gpFor neighborhood point, it is bigger than center brightness or small to distinguish neighborhood
Method beS (x)=1 (x>=0) in formula, s (x)=0 (x<0).Specifically, R=2, P=
16。
It 1.2A) is concentrated in the point of annular neighbours, those do not fall in the gray value of the pixel of pixel center position just
It is to be calculated by neighborhood pixels by bilinear interpolation.If the position of center pixel is (x, y), vicinity points gi
Position calculate are as follows:
1.3A) coding on annular neighbours' collection is carried out to operate ROR by right ring shift right, obtains LBP invariable rotary coding,
Value is the smallest to be encoded to last LBP coding.Rotation process is as follows:
1.4A) by estimate U by LBP encode in the codings of U≤2 be classified as equivalent formulations class, except equivalent formulations class with for
Mode be all classified as another kind of, referred to as mixed mode class.Estimate U to be defined as
1.5A) by rotation and normalization, the LBP mode (feature) of a width picture is P+2=18.
1B) 3D that a width size is M × N is drawn, 512 key feature points are extracted by Gist algorithm as img, generated
Feature vector FVGist, it is denoted as FVGist=(x1, x2..., x512), it is specific as follows:
1.1B) by picture gray processing and it is scaled to 128*128.
The gray level image f (x, y) that a width size is r × c 1.2B) is divided into np*npSpecification grid, then grid block number
For ng=np*np.Each grid block is successively denoted as P by rowi, wherein i=1,2 ..., ng.The size of grid block is r ' × c ',
Middle r '=r/np, c '=c/np.Specifically, r=128, c=128, np=4.
N 1.3B) is used respectivelycThe filter in a channel carries out convolutional filtering to image, then each channel filtering of each grid block
Cascade result becomes block Gist (PG) feature afterwards, i.e.,Wherein (x, y) ∈
Pi, gmn(x, y) is multiple dimensioned multi-direction Gabor filter, i.e. gmn(x, y)=α-mG (x ', y '), α > 1, wherein x '=α-m
(xcos θ+ycos θ), y '=α-m(- xsin θ+ycos θ), θ=n π/(n+1),X in formula, y are image pixel coordinates position,
σx,σyIt is the variance of the Gauss factor on the direction x and y, f respectively0It is filter centre frequency, φ is the phase difference of the harmonic factor,
α-mFor the scale factor of morther wavelet expansion, θ is rotation angle, the i.e. direction of filter.M is scale parameter, and n is direction number.GP's
Dimension is nc×r′×c′.Specifically, ncFor 4 scale, 8 direction totally 32 Gabor filters.
1.4B) to GPBy the combined result of the row referred to as overall situation Gist (GG) feature after mean value, i.e., each channel filtering result takesWhereinGGDimension be nc×ng=32*16=
512。
1C) 3D that a width size is M × N is drawn, 680 key feature points are extracted by PHOG algorithm as img, generated
Feature vector FVPHOG, it is denoted as FVPHOG=(x1, x2..., x680), it is specific as follows:
1.1C) selection first layer divides, and is divided into the small cells of 1*1.
The gradient (i.e. orientation) of each pixel in each cell 1.2C) is calculated, i.e.,I in formulax, IyMeter represents on both horizontally and vertically
Gradient value, M (x, y), θ (x, y) respectively indicate range value and the direction of gradient.
8 bin 1.3C) are divided by 360 degree, each bin includes 45 degree, and entire histogram includes 8 dimensions.Then basis
Its amplitude is added in histogram by the gradient direction of each pixel using bilinear interpolation value method.Whole picture figure is obtained at this time
Small HOG feature be 1*8=8.
1.4C) the selection second layer divides, and is divided into the small cells of 2*2.Return to step 1.2C), until the small HOG of whole picture figure
Feature is 4*8=32, into 1.5C).
1.5C) selection third layer divides, and is divided into the small cells of 4*4.Return to step 1.2C), until the small HOG of whole picture figure
Feature is 16*8=128, into 1.6C).
The 4th layer of division 1.6C) is selected, the small cells of 8*8 is divided into.Return to step 1.2C), until the small HOG of whole picture figure
Feature is 64*8=512, into 1.7C).
It is cascaded after 1.7C) four layers small HOG feature is normalized, then obtaining total characteristic is 8+32+128+512=680.
2) feature that above-mentioned three kinds of algorithms extract is merged, obtains fusion feature FV to the endmix=(FVLBP,
FVGist,FVPHOG).The number of the fusion feature is 18+512+680=1210.
3) data set is drawn to 3D using KNN classifier based on fusion feature and carries out algorithm experimental.The present invention is mainly to 3D
Picture carries out the intelligent classification based on artistic expression, thus data set according to mural painting 786, draw 138, recessed wall with drawing 800, wall
Angle 46 is constituted.Training set and test set, each classification random selection half sample are chosen by 5:5 to the 4 class data sets of 1770*1210
This is as training dataset, and remaining sample is as test set data.The random sampling test is repeated 3 times, and is averaged value and is
Report result.In the training process, distance metric is Euclidean distance.Different value is arranged to K, is tested, is taken optimal from 1 to 20
Value K=4 is classifier parameters.The above KNN algorithm model can draw emerging 3D and carry out intelligent classification.
Referring to Fig.1, the method for the present invention is embedded into 3D exhibition of paintings to look in system.The system allows user to upload 3D picture piece simultaneously
And classification displaying is carried out according to " place ", " performance " and " subject matter ".This algorithm is completed to the automatic of 3D " performance " classification drawn
Intelligent recognition.User uploads after 3D draws, system by the form of expression of automatic identification uploaded 3D picture (mural painting, draw, draw to wall or
Recessed corner), remaining classification still needs to user and manually selects.Classification results are as shown in Figure 1.
The present invention is a kind of innovative trial for combining computer with art, and the automatic mobile phone that 3D can be promoted to draw is deposited
Storage provides for designer and masses and enriches comprehensive 3D picture browsing and retrieval.In algorithm design aspect, LBP feature can be extracted
Texture in image, Gist feature can extract image space envelope, and PHOG feature can extract topography edge, these features
Fusion can comprehensively reflect mural painting, draw, draw to wall and recessed corner draw art difference, help to improve 3D draw classification standard
Exactness.
The above is only a specific embodiment of the present invention, but the design concept of the present invention is not limited to this, all to utilize this
Design makes a non-material change to the present invention, and should all belong to behavior that violates the scope of protection of the present invention.
Claims (6)
1. it is a kind of based on the 3D of Fusion Features and KNN draw intelligent classification algorithm, it is characterised in that: 1) be respectively adopted LPB algorithm,
Gist algorithm and Phog algorithm carry out the piece feature extraction of 3D picture and obtain corresponding feature vector, 2) feature vector that will be extracted
It is merged, obtains fusion feature；3) it is based on fusion feature, Intelligence Classifier is drawn using KNN algorithm building 3D；In step 1)
In, the 3D that a Zhang great little is M × N is drawn, several key feature points is extracted by invariable rotary mode LPB algorithm as img,
Generate feature vector FVLBP；
The LPB algorithm is specific as follows
1.1A) using R as the P vertex neighborhood of radius, gcCentered on, gpFor neighborhood point, neighborhood side bigger than center brightness or small is distinguished
Method isS (x)=1 in formula, x >=0；S (x)=0, x < 0；
It 1.2A) is concentrated in the point of annular neighbours, if the position of center pixel is (x, y), vicinity points giPosition calculate
Are as follows:
1.3A) coding on annular neighbours' collection is carried out to operate ROR by right ring shift right, obtains LBP invariable rotary coding, value
The smallest to be encoded to last L PB coding, rotation process is as follows:
1.4A) by estimate U by LBP encode in the codings of U≤2 be classified as equivalent formulations class, the mould in addition to equivalent formulations class
Formula is all classified as another kind of, referred to as mixed mode class, estimates U and is defined as
1.5A) by rotation and normalization, the LBP feature of a width picture is comprising P+2 characteristic point.
2. as described in claim 1 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, it is characterised in that:
In step 1), the 3D that a width size is M × N is drawn, several key feature points are extracted by Gist algorithm as img, generated special
Levy vector FVGist。
3. as claimed in claim 2 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, it is characterised in that: institute
It is specific as follows to state Gist algorithm
1.1B) by 3D figure phase gray processing and it is scaled to 128*128；
The gray level image f (x, y) that a width size is r × c 1.2B) is divided into np*npThe grid of specification, then grid block number is ng
=np*np, each grid block is successively denoted as Pi by row, wherein i=1,2 ..., ng；The size of grid block is r ' × c ', wherein r '
=r/np, c '=c/np；
N 1.3B) is used respectivelycThe filter in a channel carries out convolutional filtering to image, then cascades after each channel filtering of each grid block
Result become block Gist (PG) feature, i.e.,
Wherein (x, y) ∈ Pi, gmn(x, y)=α-mG (x ', y '), α > 1 are multiple dimensioned multi-direction Gabor filter, x '=α-m(x
Cos θ+y cos θ), y '=α-m(- x sin θ+y cos θ), θ=n π/(n+1),X in formula, y are image pixel coordinates position,
σx,σyIt is the variance of the Gauss factor on the direction x and y, f respectively0It is filter centre frequency, φ is the phase difference of harmonic factor, α-m
For the scale factor of morther wavelet expansion, θ is rotation angle, and m is scale parameter, and n is direction number, GPDimension be nc×r′×c′；
1.4B) to GPBy the combined result of the row referred to as overall situation Gist (GG) feature after mean value, i.e., each channel filtering result takesWhereinGGDimension be nc×ng。
4. as described in claim 1 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, it is characterised in that:
In step 1), the 3D that a width size is M × N is drawn, several key feature points are extracted by PHOG algorithm as img, generated special
Levy vector FVPHOG。
5. as claimed in claim 4 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, it is characterised in that: institute
It is specific as follows to state PHOG algorithm
1.1C) selection first layer divides, and is divided into the small cells of 1*1；
The gradient of each pixel in each cell 1.2C) is calculated, i.e.,I in formulax, IyRepresent the ladder on both horizontally and vertically
Angle value, M (x, y), θ (x, y) respectively indicate range value and the direction of gradient；
1.3C) being divided into 8 bin, each bin for 360 degree includes 45 degree, and entire histogram includes 8 dimensions, then according to each picture
Its amplitude is added in histogram using bilinear interpolation value method, obtains the small HOG of whole picture figure at this time by the gradient direction of vegetarian refreshments
Feature is 1*8=8；
1.4C) the selection second layer divides, and is divided into the small cells of 2*2, returns to 1.2C), until the small HOG feature of whole picture figure is 4*8
=32, into 1.5C)；
1.5C) selection third layer divides, and is divided into the small cells of 4*4, returns to 1.2C), until the small HOG feature of whole picture figure is 16*
8=128, into 1.6)；
The 4th layer of division 1.6C) is selected, the small cells of 8*8 is divided into, returns to 1.2), until the small HOG feature of whole picture figure is 64*8
=512, into 1.7)；
It is cascaded after 1.7C) four layers small HOG feature is normalized, then obtaining total characteristic is 8+32+128+512=680.
6. as described in claim 1 a kind of based on the 3D of Fusion Features and KNN picture intelligent classification algorithm, it is characterised in that:
Data set is drawn to 3D using KNN classifier based on fusion feature in step 3) and carries out algorithm experimental, specifically: data set includes wall
Draw, draw, draw to wall and recessed corner；Each classification one half-sample of random selection is as training dataset, and remaining sample is as survey
Examination collection data, the random sampling test are repeated 3 times, and are averaged as report result；In the training process, distance metric is Europe
Formula distance.
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