CN114037847A - An anti-noise local color texture feature extraction method - Google Patents

An anti-noise local color texture feature extraction method Download PDF

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CN114037847A
CN114037847A CN202111391326.8A CN202111391326A CN114037847A CN 114037847 A CN114037847 A CN 114037847A CN 202111391326 A CN202111391326 A CN 202111391326A CN 114037847 A CN114037847 A CN 114037847A
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CN114037847B (en
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束鑫
宋志刚
程科
於跃成
严熙
范燕
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Jiangsu University of Science and Technology
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Abstract

The invention discloses an anti-noise local color texture feature extraction method, which comprises the following specific steps: firstly, generating a fourth color vector channel C by a color texture image with the size of M multiplied by N according to three color channels of R-G-B according to a specific method, and arranging the fourth color vector channel C in a row to form a cube with the size of M multiplied by N multiplied by 4; then, extracting local grouping sequence mode characteristics on the 4 color channels respectively; secondly, extracting longitudinal difference value binary pattern features on 4 color channels in a channel-crossing mode; then extracting the above features by different scales, normalizing and cascading to construct a joint vector H; and finally, classifying by using the chi-square distance and the nearest neighbor classifier to obtain a classification result. The method is simple in calculation, can extract the color information in the texture image and the correlation information among the color channels, and has robustness to noise.

Description

Anti-noise local color texture feature extraction method
Technical Field
The invention relates to the technical field of image processing and pattern recognition, in particular to an anti-noise local color texture feature extraction method.
Background
Texture is a perceptible basic attribute presented on the surface of an object in nature, and texture classification is a very important research hotspot in the field of computer vision, and plays an important role in all aspects. Therefore, how to effectively acquire the characteristic texture features is the key point for image analysis and understanding.
At present, the gray texture analysis technology is more and more mature, and many gray texture descriptors have been developed and successfully applied to many fields of image classification. However, since only the grayscale image is texture classified, for color images, the color information is discarded, and is an important clue for visual perception. How to fully utilize color information while extracting the texture features of the color image has important research value and significance. Ojala et al in 2002 proposed a Local Binary Pattern (LBP), which is widely used in many application fields such as face recognition, face fraud detection, defect detection, medical image detection and the like due to its characteristics of easy implementation, low computational complexity, strong recognition capability, invariance to monotonic illumination variation and the like. Subsequently, more LBP extension optimization algorithms were proposed, however most algorithms only target gray texture images and cannot effectively process color texture images. Although the feature extraction of color texture images has made great progress, color texture still has many open problems to be solved, especially the correlation problem between different color channels and the sensitivity problem to noise. Therefore, it is important to design a descriptor that effectively utilizes color information in texture images and is robust to noise.
Disclosure of Invention
The invention aims to solve the problem that an anti-noise local color texture feature extraction method is provided, and is used for solving the problems that the traditional Local Binary Pattern (LBP) and an extended optimization algorithm thereof cannot extract color information, cannot effectively utilize correlation information among color channels, has sensitivity to noise and the like.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an anti-noise local color texture feature extraction method comprises the following steps:
the method comprises the following steps: generating a fourth color vector channel C of the color texture image with the size of M multiplied by N according to three color channels of R-G-B according to a specific method, and arranging the fourth color vector channel C in a cube with the size of M multiplied by N multiplied by 4;
step two: respectively extracting local grouping sequence mode characteristics on the 4 color channels;
step three: extracting longitudinal difference value binary pattern features on 4 color channels in a channel-crossing mode;
step four: extracting the features by adopting different scales, normalizing and cascading the features to construct a joint vector H;
step five: and classifying by using the chi-square distance and the nearest neighbor classifier to obtain a classification result.
In the first step of the present invention, a fourth color vector channel C is generated from a color texture image with size M × N according to three color channels R-G-B and placed in a cube with size M × N × 4, that is, a color texture RGB image with size M × N is generated according to a color vector formula. The total number of 4 single-channel images is R channel (red), G channel (green ), B channel (blue), and C channel (Color Vector). Placed in the order of R-G-B-C to give an M X N X4 cube. The color vector channel C is generated as follows:
Figure BDA0003368952660000021
in the second step of the present invention, local grouping sequence pattern features are extracted on 4 color channels, that is, local grouping sequence pattern features are extracted on each channel, and the extraction process is as follows: first, a dominant direction D needs to be designed. The dominant direction is defined as the index of the nearest pixel that differs most from the central pixel value, and is expressed as:
Figure BDA0003368952660000022
the maximum differential response is then used to improve the discrimination of the features and the robustness to noise. After the dominant direction D is obtained, the sequence of neighboring pixel values is circularly rotated until the pixel value with index D is located at the first position in the sequence, denoted as:
（gr，0，gr，1，…，gr，P-1)：＝(gr，D，…，gr，P-1，gr，0，…gr，D-1)
in the formula, the symbol ": "denotes an element-by-element assignment operation.
Then, the rotated neighbor pixel value sequence is uniformly divided into a plurality of groups, and in order to ensure that the texture features have lower dimensionality, the number of neighbor pixel points in each group after grouping is limited to 4, so that the group can be divided into n-P/4 groups:
Figure BDA0003368952660000025
in the formula (I), the compound is shown in the specification,
Figure BDA0003368952660000026
representing the pixel values of the ith group of neighbor pixels.
Finally, the order relationship between the neighboring pixels in each group is encoded:
LCOPr，P，i＝f(γ(g′i))
where γ (·) is a sorting function that sorts the input elements in a non-descending manner and returns their relative positions; f (-) is a mapping function that maps an input sequence to a corresponding code value. As shown in the table, the neighbor pixels in each grouping may be mapped to an integer value (i.e., LCOP code value) in the range of {0,1, …,23} according to the formula.
Figure BDA0003368952660000031
In the third step of the invention, longitudinal difference value binary pattern features are extracted in a channel-crossing mode on 4 color channels, namely, the difference values among the channels are sequentially obtained and binarized in the 4 channels according to the following formula:
V1＝VR-G＝δ(R-G)
V2＝VG-B＝δ(G-B)
V3＝VB-R＝δ(B-R)
V4＝VR-C＝δ(R-C)
V5＝VG-C＝δ(G-C)
V6＝VB-C＝δ(B-C)
Figure BDA0003368952660000032
in the third step of the invention, longitudinal difference value binary pattern features are extracted in a channel-crossing mode on 4 color channels, namely, the difference values among the channels are sequentially obtained in the 4 channels according to the following formula, and are encoded according to a certain sequence after binarization, wherein the formula is as follows:
Figure BDA0003368952660000041
in the fourth step of the invention, the characteristics are extracted by different scales, normalized and cascaded, and a combined vector H is constructed, namely the local continuous sequence mode characteristics extracted in 4 channels and the longitudinal difference value binary mode characteristics are normalized and then cascaded, wherein the normalization process is as follows:
Figure BDA0003368952660000042
Figure BDA0003368952660000043
Figure BDA0003368952660000044
Figure BDA0003368952660000045
Figure BDA0003368952660000046
then, 5 features are cascaded to obtain a final color image feature histogram:
Figure BDA0003368952660000047
the invention has the beneficial effects that:
(1) a new color channel of a color vector is added, so that texture details are enriched on the basis of the original three channels;
(2) not only the color correlation information among different channels is coded, but also the color texture information of each channel is contained;
(3) intra-channel and inter-channel features for each pixel in a color image are jointly encoded. Extracting local characteristics from four channels at a time, wherein the local characteristics comprise correlation information among different channels;
(4) is robust to noise.
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FIG. 1 is a general flow chart of a method for noise immune local color texture feature extraction of the present invention;
FIG. 2 illustrates a first step of generating a fourth color vector channel C from a color texture image of size M × N according to R-G-B color channels and arranging the fourth color vector channel C in a cube of size M × N × 4;
FIG. 3 is a diagram illustrating a step two of extracting local grouping sequential pattern features on 4 color channels according to the present invention;
FIG. 4 is a diagram illustrating a cross-channel method for extracting longitudinal difference binary pattern features on 4 color channels in step three of the present invention;
FIG. 5 is a block diagram illustrating the construction of a joint vector H in step four according to the present invention.
Detailed Description
In order to make the purpose and technical solution of the present invention clearer, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
As shown in FIG. 1, the method for extracting anti-noise local color texture features of the present invention comprises the steps of firstly generating a fourth color vector channel C according to three color channels R-G-B and a specific method for the color texture image with size of M × N, and arranging the fourth color vector channel C in a cube with size of M × N × 4; then, extracting local grouping sequence mode characteristics on the 4 color channels respectively; secondly, extracting longitudinal difference value binary pattern features on 4 color channels in a channel-crossing mode; then extracting the above features by different scales, normalizing and cascading to construct a joint vector H; and finally, classifying by using the chi-square distance and the nearest neighbor classifier to obtain a classification result. The method specifically comprises the following steps:
the method comprises the following steps: generating a fourth color vector channel C of the color texture image with the size of M multiplied by N according to three color channels of R-G-B according to a specific method, and arranging the fourth color vector channel C in a cube with the size of M multiplied by N multiplied by 4;
step two: respectively extracting local grouping sequence mode characteristics on the 4 color channels;
step three: extracting longitudinal difference value binary pattern features on 4 color channels in a channel-crossing mode;
step four: extracting the features by adopting different scales, normalizing and cascading the features to construct a joint vector H;
step five: and classifying by using the chi-square distance and the nearest neighbor classifier to obtain a classification result.
As shown in fig. 2, according to the method for extracting anti-noise local color texture features of the present invention, first, a fourth color vector channel C is generated from a color texture image with a size of M × N according to three color channels R-G-B according to a specific method, and is arranged in a cube with a size of M × N × 4, that is, a color texture RGB image with a size of M × N is generated according to a color vector formula. The total number of 4 single-channel images is R channel (red), G channel (green ), B channel (blue), and C channel (Color Vector). Placed in the order of R-G-B-C to give an M X N X4 cube. The color vector channel C is generated as follows:
Figure BDA0003368952660000051
as shown in fig. 3, in the second step of the present invention, local packet sequence pattern features are extracted on 4 color channels, that is, local packet sequence pattern features are extracted on each channel, and the extraction process is as follows: first, a dominant direction D needs to be designed. The dominant direction is defined as the index of the nearest pixel that differs most from the central pixel value, and is expressed as:
Figure BDA0003368952660000052
the maximum differential response is then used to improve the discrimination of the features and the robustness to noise. After the dominant direction D is obtained, the sequence of neighboring pixel values is circularly rotated until the pixel value with index D is located at the first position in the sequence, denoted as:
（gr，0，gr，1，…，gr，P-1)：＝(gr，D，…，gr，P-1，gr，0，…gr，D-1)
in the formula, the symbol ": "denotes an element-by-element assignment operation.
Then, the rotated neighbor pixel value sequence is uniformly divided into a plurality of groups, and in order to ensure that the texture features have lower dimensionality, the number of neighbor pixel points in each group after grouping is limited to 4, so that the group can be divided into n-P/4 groups:
Figure BDA0003368952660000063
in the formula (I), the compound is shown in the specification,
Figure BDA0003368952660000064
representing the pixel values of the ith group of neighbor pixels.
Finally, the order relationship between the neighboring pixels in each group is encoded:
LCOPr，P，i＝f(γ(g′i))
where γ (·) is a sorting function that sorts the input elements in a non-descending manner and returns their relative positions; f (-) is a mapping function that maps an input sequence to a corresponding code value. As shown in the table, the neighbor pixels in each group may be mapped to an integer value (i.e., LCOP code value) in the range of {0,1, …,23 };
Figure BDA0003368952660000065
as shown in fig. 4, in step three of the present invention, the longitudinal difference value binary pattern feature is extracted in a cross-channel manner on 4 color channels, that is, the difference values between the channels are sequentially obtained and binarized in the 4 channels according to the following formula:
V1＝VR-G＝δ(R-G)
V2＝VG-B＝δ(G-B)
V3＝VB-R＝δ(B-R)
V4＝VR-C＝δ(R-C)
V5＝VG-C＝δ(G-C)
V6＝VB-C＝δ(B-C)
Figure BDA0003368952660000071
as shown in fig. 4, in the third step of the present invention, the longitudinal difference value binary pattern feature is extracted in a channel-crossing manner on 4 color channels, that is, the difference values between the channels are sequentially obtained in the 4 channels according to the following formula, and are encoded according to a certain sequence after binarization, where the formula is as follows:
Figure BDA0003368952660000072
as shown in fig. 5, in the fourth step of the present invention, the above features are extracted by different scales, normalized and concatenated, and a joint vector H is constructed, that is, the local continuous sequence mode features extracted in 4 channels and the longitudinal difference binary mode features are normalized and concatenated, and the normalization process is as follows:
Figure BDA0003368952660000073
Figure BDA0003368952660000074
Figure BDA0003368952660000075
Figure BDA0003368952660000076
Figure BDA0003368952660000077
then, 5 features are cascaded to obtain a final color image feature histogram:
Figure BDA0003368952660000078
in order to verify the effectiveness and stability of the anti-noise local color texture feature extraction method, the specific implementation of the method is described by classifying the texture features extracted finally in fig. 5 on a standard texture library KTH-TIPS:
(1) single-scale and multi-scale performance analysis: the method analyzes the anti-noise performance under single scale and multi-scale in the KTH-TIPS database. The classification results are shown in table 1. As can be seen from table 1, the method proposed herein achieves satisfactory and stable classification accuracy at either single or multiple scales.
Table 1:
Figure BDA0003368952660000081
(2) comparing the method with other 10 texture feature extraction methods, the result is shown in table 2;
by comparison with other methods, the method provided by the invention can be verified to have good advantages over other 10 methods: the classification precision is effectively improved; color correlation information among different channels is effectively utilized, and color texture information of each channel is also included; efficient joint coding of intra-channel and inter-channel features for each pixel in a color image; the anti-noise performance is effectively improved.
Table 2:
Figure BDA0003368952660000082
the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (6)

1.一种抗噪声的局部彩色纹理特征提取方法，其特征在于：包括以下步骤：1. an anti-noise local color texture feature extraction method, is characterized in that: comprise the following steps: 步骤一：将尺寸为M×N的彩色纹理图像根据R-G-B三个颜色通道按照特定方法生成第四个颜色向量通道C并排列放置成尺寸为M×N×4的立方体；Step 1: Generate a fourth color vector channel C from the color texture image of size M×N according to the three color channels R-G-B according to a specific method and arrange it into a cube of size M×N×4; 步骤二：在4个颜色通道上面分别提取局部分组序模式特征；Step 2: Extract local grouping sequence pattern features on the 4 color channels respectively; 步骤三：在4个颜色通道上以跨通道的方式提取纵向差值二值模式特征；Step 3: Extract longitudinal difference binary mode features in a cross-channel manner on the 4 color channels; 步骤四：采用不同尺度提取上述特征归一化并级联，构建联合向量H；Step 4: Use different scales to extract the above features to normalize and cascade to construct a joint vector H; 步骤五：采用卡方距离和最近邻分类器进行分类，获取分类结果。Step 5: Use chi-square distance and nearest neighbor classifier for classification, and obtain classification results. 2.根据权利要求1所述的一种抗噪声的局部彩色纹理特征提取方法，其特征在于：步骤一中将尺寸为M×N的彩色纹理图像根据R-G-B三个颜色通道按照特定方法生成第四个颜色向量通道C并排列放置成尺寸为M×N×4的立方体，即将一张尺寸大小为M×N的彩色纹理RGB图像，根据颜色向量公式生成第四个通道C；共计4张单通道的图像，分别为R通道(red，红)，G通道(green，绿)、B通道(blue，蓝)和C通道(Color Vector)；按照R-G-B-C的排列顺序放置，从而得到一个M×N×4的立方体；颜色向量通道C的生成过程如下：2. a kind of anti-noise local color texture feature extraction method according to claim 1, is characterized in that: in step 1, the color texture image with size of M × N is generated according to a specific method according to three color channels of R-G-B, the fourth The color vector channels C are arranged and placed in a cube of size M×N×4, that is, a color texture RGB image of size M×N is used to generate the fourth channel C according to the color vector formula; a total of 4 single-channel , which are R channel (red, red), G channel (green, green), B channel (blue, blue) and C channel (Color Vector); they are placed in the order of R-G-B-C to get an M×N× 4 cubes; the generation process of color vector channel C is as follows:
Figure FDA0003368952650000011
Figure FDA0003368952650000011
 3.根据权利要求1所述的一种抗噪声的局部彩色纹理特征提取方法，其特征在于：步骤二中在4个颜色通道上面分别提取局部分组序模式特征，即在每一个通道上面提取局部分组序模式特征，提取过程如下：首先需要需要设计一个主导方向D；主导方向定义为与中心像素值差分最大的近邻像素的索引，表示为：3. a kind of anti-noise local color texture feature extraction method according to claim 1, is characterized in that: in step 2, extract local grouping sequence pattern feature respectively above 4 color channels, namely extract local part above each channel The extraction process of grouping sequential mode features is as follows: First, a dominant direction D needs to be designed; the dominant direction is defined as the index of the neighbor pixel with the largest difference from the central pixel value, which is expressed as:
Figure FDA0003368952650000012
Figure FDA0003368952650000012
 接着采用最大差分响应可以改善特征的鉴别力以及对噪声的鲁棒性；在获得主导方向D之后，对近邻像素值序列进行圆周性旋转，直到索引为D的像素值位于序列中的第一个位置，表示为：Then the maximum differential response can be used to improve the discrimination of features and the robustness to noise; after obtaining the dominant direction D, the sequence of neighboring pixel values is rotated circularly until the pixel value with index D is located at the first pixel value in the sequence location, expressed as: (gr，0，gr，1，…，gr，P-1)：＝(gr，D，…，gr，P-1，gr，0，…gr，D-1)( gr,0 , gr,1 ,..., gr,P-1 ):=( gr,D ,..., gr,P-1 , gr,0 ,... gr,D-1 ) 式中，符号“：＝”表示逐元素赋值操作；In the formula, the symbol ":=" represents the element-by-element assignment operation; 然后，将旋转后的近邻像素值序列均匀地划分为若干组，为保证纹理特征具有较低的维度，将分组后每组中的近邻像素点个数限制为4，因此分为n＝P/4组：Then, the rotated adjacent pixel value sequence is evenly divided into several groups, in order to ensure that the texture features have a lower dimension, the number of adjacent pixels in each group after grouping is limited to 4, so it is divided into n=P/ 4 groups:
Figure FDA0003368952650000021
Figure FDA0003368952650000021
 式中，
Figure FDA0003368952650000022
表示第i组近邻像素的像素值；In the formula,
Figure FDA0003368952650000022
Represents the pixel value of the ith group of neighboring pixels; 最后，编码每组中近邻像素之间的序关系：Finally, encode the order relationship between the neighboring pixels in each group: LCOPr，P，i＝f(γ(g′i))LCOP r,P,i =f(γ(g′ i )) 式中，γ(·)是排序函数，它以非降序的方式对输入元素进行排序并返回它们的相对位置；f(·)是映射函数，它将输入序列映射到对应的码值；如表所示，根据公式，每个分组中的近邻像素可以映射到一个整数值(即LCOP码值)，所述整数值的范围是{0,1,…,23}；In the formula, γ(·) is the sorting function, which sorts the input elements in a non-descending manner and returns their relative positions; f(·) is the mapping function, which maps the input sequence to the corresponding code value; as shown in the table As shown, according to the formula, the neighboring pixels in each group can be mapped to an integer value (ie, the LCOP code value), and the range of the integer value is {0,1,...,23};
Figure FDA0003368952650000023
Figure FDA0003368952650000023
 4.根据权利要求1所述的一种抗噪声的局部彩色纹理特征提取方法，其特征在于：步骤三中在4个颜色通道上以跨通道的方式提取纵向差值二值模式特征，即在4个通道中按照下列公式顺序获得通道之间的差值并二值化，公式如下：4. a kind of anti-noise local color texture feature extraction method according to claim 1, is characterized in that: in step 3, on 4 color channels, the longitudinal difference binary pattern feature is extracted in the mode of cross-channel, namely in 4 color channels. In the 4 channels, the difference between the channels is obtained and binarized according to the following formula. The formula is as follows: V1＝VR-G＝δ(R-G)V 1 =V RG =δ(RG) V2＝VG-B＝δ(G-B)V 2 =V GB =δ(GB) V3＝VB-R＝δ(B-R)V 3 =V BR =δ(BR) V4＝VR-C＝δ(R-C)V 4 =V RC =δ(RC) V5＝VG-C＝δ(G-C)V 5 =V GC =δ(GC) V6＝VB-C＝δ(B-C)V 6 =V BC =δ(BC)
Figure FDA0003368952650000031
Figure FDA0003368952650000031
 5.根据权利要求4所述的一种抗噪声的局部彩色纹理特征提取方法，其特征在于：步骤三中在4个颜色通道上以跨通道的方式提取纵向差值二值模式特征，即在4个通道中按照下列公式顺序获得通道之间的差值并二值化后按照一定的顺序编码，公式如下：5. a kind of anti-noise local color texture feature extraction method according to claim 4, is characterized in that: in step 3, on 4 color channels, the longitudinal difference binary pattern feature is extracted in a cross-channel mode, that is, in step 3. In the 4 channels, the difference between the channels is obtained according to the following formula sequence and binarized and encoded in a certain order. The formula is as follows:
Figure FDA0003368952650000032
Figure FDA0003368952650000032
 6.根据权利要求3、4或5所述的一种抗噪声的局部彩色纹理特征提取方法，其特征在于：步骤四中采用不同尺度提取上述特征归一化并级联，构建联合向量H，即将在4个通道中提取到的局部连续序模式特征与纵向差值二值模式特征归一化后级联，归一化过程如下：6. a kind of anti-noise local color texture feature extraction method according to claim 3,4 or 5, is characterized in that: in step 4, adopt different scales to extract above-mentioned feature normalization and cascade, build joint vector H, The local continuous sequence pattern features extracted from the four channels and the longitudinal difference binary pattern features are normalized and cascaded. The normalization process is as follows:
Figure FDA0003368952650000033
Figure FDA0003368952650000033
Figure FDA0003368952650000034
Figure FDA0003368952650000034
Figure FDA0003368952650000035
Figure FDA0003368952650000035
Figure FDA0003368952650000036
Figure FDA0003368952650000036
Figure FDA0003368952650000037
Figure FDA0003368952650000037
 之后将5个特征级联，得到最终的彩色图像特征直方图：Then 5 features are cascaded to get the final color image feature histogram:
Figure FDA0003368952650000038
Figure FDA0003368952650000038
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* Cited by examiner, † Cited by third party
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CN109740572A (en) * 2019-01-23 2019-05-10 浙江理工大学 A face detection method based on local color texture features
CN111696080A (en) * 2020-05-18 2020-09-22 江苏科技大学 Face fraud detection method, system and storage medium based on static texture
CN112508038A (en) * 2020-12-03 2021-03-16 江苏科技大学 Cross-channel local binary pattern color texture classification method

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* Cited by examiner, † Cited by third party
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CN109740572A (en) * 2019-01-23 2019-05-10 浙江理工大学 A face detection method based on local color texture features
CN111696080A (en) * 2020-05-18 2020-09-22 江苏科技大学 Face fraud detection method, system and storage medium based on static texture
CN112508038A (en) * 2020-12-03 2021-03-16 江苏科技大学 Cross-channel local binary pattern color texture classification method

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