CN106203528B - It is a kind of that intelligent classification algorithm is drawn based on the 3D of Fusion Features and KNN - Google Patents

Abstract

A 3D painting intelligent classification algorithm based on feature fusion and KNN, comprising the following steps: 1) using Lbp algorithm, Gist algorithm and Phog algorithm to extract 3D painting picture features to obtain corresponding feature vectors; 2) extracting the extracted feature vectors Fusion is performed to obtain fusion features; 3) Based on the fusion features, KNN algorithm is used to construct a 3D painting intelligent classifier. The invention is an innovative attempt to combine computer and art, which can promote automatic mobile phone storage of 3D paintings, and provide designers and the public with rich and comprehensive browsing and retrieval of 3D paintings. In terms of algorithm design, the LBP feature can extract the texture in the image, the Gist feature can extract the image space envelope, and the PHOG feature can extract the local image edge. The artistic distinction of concave corner paintings helps to improve the accuracy of 3D painting classification.

Description

An Intelligent Classification Algorithm of 3D Painting Based on Feature Fusion and KNN

technical field

The invention relates to the field of 3D painting classification, in particular to a 3D painting intelligent classification algorithm based on feature fusion and KNN.

Background technique

In recent years, naked-eye 3D paintings have attracted more and more attention and sought after due to their special artistic expression, strong visual shock and very interesting interactivity, covering advertising, exhibitions, home furnishing and other fields. development prospects. In general, 3D paintings are classified according to their artistic expressions, such as wall paintings, floor paintings, wall paintings, concave wall paintings, etc. However, with the popular development and commercial application of 3D painting art, more and more works are created by artists. How to effectively collect and summarize a large number of 3D paintings, build a 3D painting database, and manage and retrieve 3D paintings accurately and quickly has become an urgent need for the 3D painting design and application industry. For services in the vertical field of 3D painting, no enterprises and organizations have yet carried out related work.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to propose an intelligent classification method for 3D paintings based on feature fusion and KNN, which can intelligently identify and classify pictures uploaded by users based on artistic expressions, and is helpful for the construction of an automatic storage system for 3D paintings.

The present invention adopts following technical scheme:

A 3D painting intelligent classification algorithm based on feature fusion and KNN, which is characterized in that: 1) using Lbp algorithm, Gist algorithm and Phog algorithm to extract 3D painting image features to obtain corresponding feature vectors, 2) extracting the extracted feature vectors Fusion is performed to obtain fusion features; 3) Based on the fusion features, KNN algorithm is used to construct a 3D painting intelligent classifier.

Preferably, in step 1), a 3D drawing image img with a size of M×N is used to extract several key feature points through a rotation invariant mode LPB algorithm to generate a feature vector FV _LBP .

Preferably, the LPB algorithm is specifically as follows

1.1A) The P point neighborhood with R as the radius, g _c as the center, and g _p as the neighborhood point, the method to distinguish whether the neighborhood is larger or smaller than the center brightness is: where s(x)=1(x≥0), s(x)=0(x<0);

1.2A) In the point set of circular neighbors, if the position of the central pixel is (x, y), the position of the adjacent pixel _gi is calculated as:

1.3A) Perform the ROR operation on the code on the circular neighbor set to obtain the LBP rotation invariant code, and the code with the smallest value is the last LBP code. The rotation operation is as follows:

1.4A) The code with U≤2 in LBP coding is classified into the equivalent pattern class by the measure U, and the patterns other than the equivalent pattern class are classified into another class, which is called the mixed pattern class, and the measure U is defined as

1.5A) After rotation and normalization, the LBP feature of a picture contains P+2 feature points.

Preferably, in step 1), a 3D painting image img with a size of M×N is used to extract several key feature points through the Gist algorithm to generate a feature vector FV _Gist .

5. A 3D painting intelligent classification algorithm based on feature fusion and KNN according to claim 4, wherein the Gist algorithm is as follows:

1.1B) Grayscale and scale the 3D image to 128*128;

1.2B) Divide a grayscale image f(x, y) of size r×c into grids of n _p *n _p specifications, then the number of grid blocks is n _g =n _p *n _p . The grid blocks are denoted as Pi in row order, where _i =1,2,...,n _g ; the size of grid blocks is r′×c′, where r′=r/n _p ,c′=c/ n _p ;

1.3B) Perform convolution filtering on the image with filters of n _c channels respectively, then the result of concatenation after filtering of each channel of each grid block becomes the block Gist (PG) feature, that is,

where (x,y)∈P _i , g _mn (x,y)=α- ^m g(x′,y′),α>1 is a multi-scale and multi-directional Gabor filter, x′=α- ^m (xcosθ +ycosθ),y′=α- ^m (-xsinθ+ycosθ),θ=nπ/(n+1), where x and y are the image pixel coordinate positions, σ _x , σ _y are the variances of the Gaussian factors in the x and y directions, respectively, f ₀ is the center frequency of the filter, φ is the phase difference of the harmonic factor, and α ^-m is The scale factor of the mother wavelet dilation, θ is the rotation angle, m is the scale number, n is the direction number, and the dimension of G ^P is n _c ×r′×c′;

1.4B) The result of taking the average of the filtering results of each channel of G ^P and then combining them by row is called the global Gist (GG) feature, that is, in The dimension of G ^G is n _c ×n _g .

Preferably, in step 1), a 3D painting image img with a size of M×N is used to extract several key feature points through the PHOG algorithm to generate a feature vector FV _PHOG .

Preferably, the PHOG algorithm is specifically as follows

1.1C) Select the first layer division and divide it into 1*1 small cells;

1.2C) Calculate the gradient of each pixel in each cell, ie where I _x and I _y represent the gradient values in the horizontal and vertical directions, and M(x, y) and θ(x, y) represent the magnitude and direction of the gradient, respectively;

1.3C) Divide 360 degrees into 8 bins, each bin contains 45 degrees, and the entire histogram contains 8 dimensions, and then according to the gradient direction of each pixel point, the bilinear interpolation method is used to accumulate its amplitude to In the histogram, the small HOG feature of the whole image is obtained at this time as 1*8=8;

1.4C) Select the second layer division, divide it into 2*2 small cells, go back to 1.2C), until the small HOG feature of the whole picture is 4*8=32, enter 1.5C);

1.5C) Select the third layer division, divide it into 4*4 small cells, return to 1.2C), until the small HOG feature of the whole picture is 16*8=128, enter 1.6);

1.6C) Select the fourth layer division, divide it into 8*8 small cells, go back to 1.2), until the small HOG feature of the whole picture is 64*8=512, enter 1.7);

1.7C) After normalizing and concatenating the four-layer small HOG features, the total features are 8+32+128+512=680.

Preferably, in step 3), based on the fusion feature, KNN classifier is used to perform algorithm experiments on the 3D painting data set, specifically: the data set includes wall paintings, floor paintings, wall and floor paintings and concave wall corners; each classification randomly selects half of the samples as The training data set, the remaining samples are used as the test set data, the random sampling experiment is repeated 3 times, and the average value is taken as the report result; in the training process, the distance metric is the Euclidean distance.

As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following beneficial effects:

The invention is an innovative attempt to combine computer and art, which can promote automatic mobile phone storage of 3D paintings, and provide designers and the public with rich and comprehensive browsing and retrieval of 3D paintings. In terms of algorithm design, the LBP feature can extract the texture in the image, the Gist feature can extract the image space envelope, and the PHOG feature can extract the local image edge. The artistic distinction of concave corner paintings helps to improve the accuracy of 3D painting classification.

Description of drawings

Figure 1 is a schematic diagram of the classification results of the present invention.

Detailed ways

The present invention will be further described below through specific embodiments.

A 3D painting intelligent classification algorithm based on feature fusion and KNN, including the following steps

1) The Lbp algorithm, the Gist algorithm and the Phog algorithm are respectively used to extract the features of the 3D pictures to obtain the corresponding feature vectors, as follows:

1A) Extract 18 key feature points from a 3D painting image img with a size of M×N through the rotation invariant mode LPB algorithm to generate a feature vector FV _LBP , denoted as FV _LBP = (x ₁ , x ₂ ,...,x ₁₈ ), as follows:

1.1A) The P point neighborhood with R as the radius, g _c as the center, and g _p as the neighborhood point, the method to distinguish whether the neighborhood is larger or smaller than the center brightness is: In the formula, s(x)=1 (x≥0), s(x)=0 (x<0). Specifically, R=2, P=16.

1.2A) In the point set of circular neighbors, the gray values of those pixels that do not just fall in the center of the pixel are calculated by bilinear interpolation from neighboring pixels. If the position of the center pixel is (x, y), the position of the adjacent pixel _gi is calculated as:

1.3A) Perform the ROR operation on the code on the circular neighbor set to obtain the LBP rotation invariant code, and the code with the smallest value is the last LBP code. The rotation operation is as follows:

1.4A) The codes with U≤2 in LBP coding are classified into the equivalent mode class by measuring U, and the modes except the equivalent mode class are classified into another class, which is called the mixed mode class. The measure U is defined as

1.5A) After rotation and normalization, the LBP mode (feature) of a picture is P+2=18.

1B) Extract 512 key feature points from a 3D painting image img with a size of M×N through the Gist algorithm, and generate a feature vector FV _Gist , denoted as FV _Gist = (x ₁ , x ₂ , . . . , x ₅₁₂ ), specifically as follows:

1.1B) Grayscale and scale the image to 128*128.

1.2B) Divide a grayscale image f(x, y) with a size of r×c into a standard grid of n _p *n _p , then the number of grid blocks is n _g =n _p *n _p . Each grid block is denoted as P _i row by row, where i=1,2,..., _ng . The size of the grid block is r'xc', where r'=r/ _np and c'=c/ _np . Specifically, r=128, c=128, _np =4.

1.3B) Perform convolution filtering on the image with filters of n _c channels respectively, then the result of concatenation after filtering of each channel of each grid block becomes the block Gist (PG) feature, that is, where (x,y)∈P _i , g _mn (x,y) is a multi-scale and multi-directional Gabor filter, that is, g _mn (x, y)=α- ^m g(x′,y′),α>1 , where x′=α- ^m (xcosθ+ycosθ), y′=α- ^m (-xsinθ+ycosθ), θ=nπ/(n+1), where x and y are the image pixel coordinate positions, σ _x , σ _y are the variances of the Gaussian factors in the x and y directions, respectively, f ₀ is the center frequency of the filter, φ is the phase difference of the harmonic factor, and α ^-m is The scale factor of the mother wavelet expansion, θ is the rotation angle, that is, the direction of the filter. m is the number of scales, and n is the number of directions. The dimension of G ^P is n _c ×r′×c′. Specifically, n _c is a total of 32 Gabor filters in 4 scales and 8 directions.

1.4B) The result of taking the average of the filtering results of each channel of G ^P and then combining them by row is called the global Gist (GG) feature, that is, in The dimension of G ^G is n _c ×n _g =32*16=512.

1C) Extract 680 key feature points from a 3D painting image img with a size of M×N through the PHOG algorithm to generate a feature vector FV _PHOG , denoted as FV _PHOG = (x ₁ , x ₂ , . . . , x ₆₈₀ ), specifically as follows:

1.1C) Select the first layer division and divide it into 1*1 small cells.

1.2C) Calculate the gradient (ie orientation) of each pixel in each cell, ie In the formula, I _x and I _y represent the gradient values in the horizontal and vertical directions, and M(x, y) and θ(x, y) represent the magnitude and direction of the gradient, respectively.

1.3C) Divide 360 degrees into 8 bins, each bin contains 45 degrees, and the entire histogram contains 8 dimensions. Then according to the gradient direction of each pixel point, its amplitude is accumulated into the histogram using bilinear interpolation method. At this time, the small HOG feature of the whole image is obtained as 1*8=8.

1.4C) Select the second layer division and divide it into 2*2 small cells. Go back to step 1.2C), until the small HOG feature of the whole image is 4*8=32, and enter 1.5C).

1.5C) Select the third layer division and divide it into 4*4 small cells. Go back to step 1.2C), until the small HOG feature of the whole picture is 16*8=128, and enter 1.6C).

1.6C) Select the fourth layer to divide into 8*8 small cells. Go back to step 1.2C), until the small HOG feature of the whole picture is 64*8=512, and enter 1.7C).

1.7C) After normalizing and concatenating the four-layer small HOG features, the total features are 8+32+128+512=680.

2) The features extracted by the above three algorithms are fused to obtain the final fusion feature FV _mix =(FV _LBP , FV _Gist , FV _PHOG ). The number of the fusion features is 18+512+680=1210.

3) Based on the fusion features, the KNN classifier is used to perform algorithm experiments on the 3D painting dataset. The present invention mainly performs intelligent classification of 3D paintings based on artistic expressions, so the data set is composed of wall paintings 786, floor paintings 800, wall and floor paintings 138, and concave wall corners 46. For the 1770*1210 4 types of data sets, the training set and the test set are selected at a ratio of 5:5, and half of the samples are randomly selected as the training data set for each classification, and the remaining samples are used as the test set data. The random sampling experiment was repeated 3 times, and the average value was taken as the reported result. During training, the distance metric is Euclidean distance. Different values are set for K, from 1 to 20 for experiments, and the optimal value K=4 is taken as the classifier parameter. The above KNN algorithm model can intelligently classify new 3D paintings.

Referring to Fig. 1, the method of the present invention is embedded in a 3D painting exhibition system. The system allows users to upload 3D pictures and display them in categories according to "place", "performance" and "theme". This algorithm completes the automatic and intelligent identification of the "performance" category of 3D paintings. After the user uploads the 3D painting, the system will automatically identify the representation of the uploaded 3D painting (wall painting, floor painting, wall painting or concave wall corner), and the user still needs to manually select the remaining categories. The classification results are shown in Figure 1.

The invention is an innovative attempt to combine computer and art, which can promote automatic mobile phone storage of 3D paintings, and provide designers and the public with rich and comprehensive browsing and retrieval of 3D paintings. In terms of algorithm design, the LBP feature can extract the texture in the image, the Gist feature can extract the image space envelope, and the PHOG feature can extract the local image edge. The artistic distinction of concave corner paintings helps to improve the accuracy of 3D painting classification.

The above are only specific embodiments of the present invention, but the design concept of the present invention is not limited to this, and any non-substantial modification of the present invention by using this concept should be regarded as an act of infringing the protection scope 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 FV_LBP；

The LPB algorithm is specific as follows

1.1A) using R as the P vertex neighborhood of radius, g_cCentered on, g_pFor 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 g_iPosition 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 FV_Gist。

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 n_p*n_pThe grid of specification, then grid block number is n_g =n_p*n_p, each grid block is successively denoted as Pi by row, wherein i=1,2 ..., n_g；The size of grid block is r ' × c ', wherein r ' =r/n_p, c '=c/n_p；

N 1.3B) is used respectively_cThe 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) ∈ P_i, g_mn(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, σ_x,σ_yIt 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, G^PDimension be n_c×r′×c′；

1.4B) to G^PBy the combined result of the row referred to as overall situation Gist (GG) feature after mean value, i.e., each channel filtering result takesWhereinG^GDimension be n_c×n_g。

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 FV_PHOG。

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 formula_x, I_yRepresent 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.