WO2017166586A1 - Procédé et système d'identification d'images basés sur un réseau neuronal convolutif, et dispositif électronique - Google Patents

Procédé et système d'identification d'images basés sur un réseau neuronal convolutif, et dispositif électronique Download PDF

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WO2017166586A1
WO2017166586A1 PCT/CN2016/096031 CN2016096031W WO2017166586A1 WO 2017166586 A1 WO2017166586 A1 WO 2017166586A1 CN 2016096031 W CN2016096031 W CN 2016096031W WO 2017166586 A1 WO2017166586 A1 WO 2017166586A1
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layer
convolution
layers
pooling
image
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PCT/CN2016/096031
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Chinese (zh)
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刘阳
白茂生
魏伟
蔡砚刚
祁海
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乐视控股（北京）有限公司
乐视云计算有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features

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  • the invention relates to the field of convolutional neural network technology, in particular to a picture identification method, system and electronic device based on a convolutional neural network.
  • CNN Convolutional Neural Network
  • the basic structure of a convolutional neural network includes a plurality of convolutional layers, each of which is provided with a plurality of neurons, and the input of each neuron is locally receptive to the previous convolutional layer (local receptive Filed) is concatenated by convolving the data of the locally accepted domain of the previous convolutional layer to extract the features of the locally accepted domain. Once the local feature is extracted, its positional relationship with other features is also followed. Determined; then, by performing local averaging (also known as pooling processing) and quadratic feature extraction for feature mapping, feature information is obtained, and the feature information is output to the next convolution layer to continue processing until the last layer is reached ( Output layer) to get the final output.
  • local averaging also known as pooling processing
  • quadratic feature extraction for feature mapping feature information is obtained, and the feature information is output to the next convolution layer to continue processing until the last layer is reached ( Output layer) to get the final output.
  • Feature mapping usually uses the sigmoid function as the activation function of the convolutional neural network.
  • a convolutional neural network neurons on one convolutional layer share weight with other neurons in the same layer, thus reducing the number of network free parameters.
  • an activation function can be applied to each data value as an output to determine whether a threshold is reached, and thus the resulting data value is used as an input to the next convolutional layer.
  • a convolutional neural network calculation model for identification includes a convolutional layer, a pooling layer, and a full The connection layer and subsequent classifiers. By training the existing sample data, a better convolutional neural network calculation model can be obtained. When it is necessary to identify a new target, only the target data needs to be input into the calculation model, and the recognition of the new target can be realized.
  • the existing computational model using convolutional neural networks is used for target identification, it is usually calculated according to the existing fixed model architecture, such as AlexNet, VGG, GoogLeNet, etc.
  • the convolutional layer, the pooling layer, and the whole Parameters and architectures such as the connection layer and the activation function have been fixed.
  • they are versatile, they also make the recognition results poor when applied to specific scenarios. For example, in the yellowing of videos or pictures, the effect of discrimination is poor.
  • the object of the present invention is to provide a picture identification method and system based on convolutional neural network, which can greatly improve the speed and accuracy of picture authentication.
  • a method for discriminating a picture based on a convolutional neural network comprising:
  • the image data to be identified is input into at least two concatenated layers connected in series for continuous extraction of features, to obtain feature data after image extraction, wherein the core sizes of the at least two convolution layers are no more than 5 ⁇ 5;
  • the two-dimensional feature values are classified by a classifier to obtain a discrimination result of the picture.
  • the at least two concatenated concatenated layers comprise four convoluted layer C1 layers, C2 layers, C3 layers and C4 layers connected in sequence, and the core sizes of the convolution layers are respectively: C1 layer The core size is 3 ⁇ 3, the core size of the C2 layer is 3 ⁇ 3, the core size of the C3 layer is 5 ⁇ 5, and the core size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four successively connected convolution layers is 1; the number of convolution kernels of the four convolutional layers is 96; the pad values of the C1 layer and the C2 layer are both 1, The pad values of the C3 layer and the C4 layer are both 2.
  • the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain feature data of the reduced dimension of the image.
  • the steps include:
  • the core size of the pooling layer P4, the pooling layer P5, and the pooling layer P8 are all 3, the number of steps is 2, and the pad value is 0.
  • the core size of the convolution layer C5 is 5, and the number of steps is 1, the pad value is 2, the number of convolution kernels is 256; the cores of the convolution layer C6, the convolution layer C7, and the convolution layer C8 are all 3, the number of steps is 1, and the pad values are all 1.
  • the number of convolution kernels is 384, 384, and 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer is 2048, 2048, respectively 2048, 2; and all connected layers use the dropout method for data processing.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, the pooling layer P5, and the convolution.
  • the processing of layer C6, convolution layer C7, convolution layer C8, pooling layer P8, fully connected layer fc9, fully connected layer fc10, fully connected layer fc11, fully connected layer fc12, and then connected to the classifier SVM is classified , get the identification result of the picture.
  • all of the convolutional layers and all of the fully connected layers perform activation processing of data by using an activation function LEAKY RELU.
  • the invention also provides a picture identification system based on convolutional neural network, comprising:
  • a data extraction module configured to input the image data to be identified into at least two concatenated layers connected in series to perform continuous feature extraction, obtain feature data after image extraction, and send the feature data extracted by the image to the data dimensionality reduction module.
  • the core sizes of the at least two convolution layers are no more than 5 ⁇ 5;
  • a data dimension reduction module configured to receive the feature data extracted by the image extraction module, and perform the feature data by using the at least one pooled layer and the at least one convolution layer Dimensionality reduction and feature data extraction, obtaining feature data after dimension reduction of the image, and transmitting the feature data of the obtained image reduced dimension to the fully connected module; wherein the pooling layer adopts an average pooling;
  • a full connection module configured to receive feature data of the reduced dimension of the image sent by the feature dimension reduction module, and input the feature data of the reduced dimension of the image into at least one fully connected layer to obtain a two-dimensional feature value of the image data; Sending the obtained two-dimensional feature value of the picture data to the classification module;
  • the classification module is configured to receive the two-dimensional feature value of the picture data sent by the fully connected module, and classify the two-dimensional feature value by using a classifier to obtain a picture identification result.
  • the data extraction module includes:
  • C1 layer core size is 3 ⁇ 3
  • C2 layer core size is 3 ⁇ 3
  • the size of the C3 layer core is 5 ⁇ 5
  • the size of the C4 layer core is 5 ⁇ 5.
  • the number of steps of the four successively connected convolution layers is 1; the number of convolution kernels of the four convolutional layers is 96; the pad values of the C1 layer and the C2 layer are both 1, The pad values of the C3 layer and the C4 layer are both 2.
  • the data dimension reduction module includes:
  • a pooling layer P4 a convolution layer C5, a pooling layer P5, a convolution layer C6, a convolution layer C7, a convolution layer C8, and a pooling layer P8 connected in sequence; wherein the pooling layer P4 and the pooling layer P5, the pooling layer P8 has a kernel size of 3, the number of steps is 2, and the pad value is 0; the convolutional layer C5 has a kernel size of 5, a step number of 1, a pad value of 2, and a convolution kernel.
  • the number of the convolutional layer C6, the convolutional layer C7, and the convolutional layer C8 is 3, the number of steps is 1, the pad value is 1, and the number of convolution kernels is 384, 384, 256.
  • the fully connected module includes:
  • the system includes a convolution layer C1, a convolution layer C2, a convolution layer C3, a convolution layer C4, a pooling layer P4, a convolution layer C5, a pooling layer P5, and a convolution layer C6 which are sequentially connected.
  • the processing of the convolutional layer C7, the convolutional layer C8, the pooling layer P8, the fully connected layer fc9, the fully connected layer fc10, the fully connected layer fc11, and the fully connected layer fc12 is then connected to the classifier SVM for classification processing. The result of the identification of the picture.
  • all of the convolutional layers and all of the fully connected layers perform activation processing of data by using an activation function LEAKY RELU.
  • Embodiments of the invention further disclose an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor The instruction is executed by the at least one processor to enable the at least one processor to input the picture data to be identified into at least two concatenated layers connected in series for continuous extraction of features, to obtain features after image extraction Data; extracting the feature data of the image through at least one pooling layer and at least one convolution layer to perform dimension reduction of the feature data and feature data extraction, to obtain feature data after the dimension reduction of the image; wherein the pooling layer Using average pooling; the picture will be The dimensioned data is input into at least one fully connected layer to obtain a two-dimensional feature value of the picture data; and the two-dimensional feature value is classified by the classifier to obtain a picture identification result.
  • the at least two concatenated layers connected in series comprise four convoluted layer C1 layers, C2 layers, C3 layers and C4 layers connected in series, and the core sizes of the convolution layers are respectively
  • the core size of the C1 layer is 3 ⁇ 3
  • the core size of the C2 layer is 3 ⁇ 3
  • the core size of the C3 layer is 5 ⁇ 5
  • the core size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four sequentially connected convolution layers is 1; the number of convolution kernels of the four convolution layers is 96; the pads of the C1 layer and the C2 layer The values are all 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain a dimension reduction of the image.
  • the step of characterizing data includes: passing the extracted feature data through the pooled layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, the convolution layer C7, the convolution layer C8, and the pool sequentially connected.
  • the layer P8 wherein, the pooling layer P4, the pooling layer P5, and the pooling layer P8 have a kernel size of 3, the number of steps is 2, and the pad value is 0; the core size of the convolution layer C5 5, the number of steps is 1, the pad value is 2, and the number of convolution kernels is 256; the cores of the convolutional layer C6, the convolutional layer C7, and the convolutional layer C8 are all 3, and the number of steps is 1, the pad value is 1, and the number of convolution kernels is 384, 384, 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer is 2048 , 2048, 2048, 2; and all connected layers use the dropout method for data processing.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, and the pooling layer P5.
  • the present invention also discloses a non-volatile computer storage medium, wherein the storage medium stores computer-executable instructions that, when executed by an electronic device, cause an electronic device
  • the image data to be identified is input into at least two convolution layers connected in series to perform continuous feature extraction, and the feature data after the image extraction is obtained; and the feature data extracted by the image is passed through at least one pooling layer and at least A convolution layer performs dimension reduction of the feature data and feature data extraction to obtain feature data after the dimension reduction of the image; wherein the pooling layer adopts an average pooling; and the feature data of the reduced dimension of the image is input into at least one In the fully connected layer, a two-dimensional feature value of the picture data is obtained; and the two-dimensional feature value is classified by the classifier to obtain a picture identification result.
  • the above storage medium wherein the at least two concatenated layers connected in series comprise four convoluted layer C1 layers, C2 layers, C3 layers and C4 layers connected in series, and the core sizes of the convolution layers are respectively
  • the core size of the C1 layer is 3 ⁇ 3
  • the core size of the C2 layer is 3 ⁇ 3
  • the core size of the C3 layer is 5 ⁇ 5
  • the core size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four sequentially connected convolution layers is 1; the number of convolution kernels of the four convolution layers is 96; the pads of the C1 layer and the C2 layer The values are all 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain a dimension reduction of the image.
  • the step of characterizing data includes: passing the extracted feature data through the pooled layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, the convolution layer C7, the convolution layer C8, and the pool sequentially connected.
  • the layer P8 wherein, the pooling layer P4, the pooling layer P5, and the pooling layer P8 have a kernel size of 3, the number of steps is 2, and the pad value is 0; the core size of the convolution layer C5 5, the number of steps is 1, the pad value is 2, and the number of convolution kernels is 256; the cores of the convolutional layer C6, the convolutional layer C7, and the convolutional layer C8 are all 3, and the number of steps is 1, the pad value is 1, and the number of convolution kernels is 384, 384, 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer is 2048 , 2048, 2048, 2; and all connected layers use the dropout method for data processing.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, and the pooling layer P5.
  • Embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer
  • the computer is caused to perform the method of any of the above.
  • the convolutional neural network-based image discriminating method and system provided by the embodiments of the present invention are first connected through a convolution layer of a plurality of small windows (cores of the convolutional layer), so that Quickly extracting local features of the image and quickly combining these local features into advanced features can greatly improve the speed and efficiency of image recognition.
  • the image discriminating method and system based on the convolutional neural network uses the average pooling and the processing of the all-connected layer to make the final output of the image data into two features, thereby enabling the classifier to perform classification and classification.
  • the image discriminating method and system based on the convolutional neural network uses the average pooling and the processing of the all-connected layer to make the final output of the image data into two features, thereby enabling the classifier to perform classification and classification. When processed, not only is it faster, but it is more accurate.
  • FIG. 1 is a flowchart of an embodiment of a method for discriminating a picture based on a convolutional neural network according to the present invention
  • FIG. 2 is a schematic structural diagram of a convolutional neural network calculation model provided by the present invention.
  • FIG. 3 is a schematic structural diagram of an embodiment of a convolutional neural network-based picture authentication system according to the present invention.
  • FIG. 4 is a schematic structural diagram of hardware of an electronic device according to an embodiment of the present invention.
  • connection or integral connection; may be mechanical connection or electrical connection; may be directly connected, may also be indirectly connected through an intermediate medium, or may be internal communication of two components, may be wireless connection, or may be wired connection.
  • connection or integral connection; may be mechanical connection or electrical connection; may be directly connected, may also be indirectly connected through an intermediate medium, or may be internal communication of two components, may be wireless connection, or may be wired connection.
  • FIG. 1 a flow chart of an embodiment of a convolutional neural network based picture authentication method provided by the present invention is shown.
  • the method for image identification based on a convolutional neural network includes:
  • Step 101 Input the image data to be identified into at least two concatenated layers connected in series to perform continuous extraction of features, and obtain feature data after image extraction, wherein, preferably, the core sizes of the at least two convolution layers are Not more than 5 ⁇ 5;
  • the picture data to be identified may be direct picture data information, or may be picture information acquired in the video, that is, the method according to the present invention is also applicable to video identification.
  • the convolution layer is used to extract the local block features of the input picture data to obtain a higher level of feature data, and multiple convolution operations are performed in each convolution layer.
  • the core of the convolution layer adopts an n ⁇ n structure (m ⁇ n may also be used), and the smaller the core of the convolution layer, the more features can be extracted, but the corresponding feature data is also more.
  • step 102 the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain feature data after dimension reduction of the image;
  • the pooling layer adopts an average pooling;
  • the pooling layer is used for performing dimensionality reduction processing on the feature data outputted by the convolution layer, that is, the data amount is greatly reduced on the basis of ensuring the validity of the data.
  • Repeat here refers to repeated pooling or convolution
  • the process for example: pooling layer - convolution layer - pooling layer - convolution layer, of course, it is also possible to have a pooling layer or a convolution layer multiple times in a certain layer in the middle.
  • the average pooling refers to taking the average value of the data within the size range of the pooling kernel as the output data after pooling according to the principle of pooling.
  • Step 103 Enter feature data of the reduced-dimensionality of the picture into at least one fully connected layer to obtain a two-dimensional feature value of the picture data.
  • the last fully connected layer outputs a 2-dimensional feature data, which makes it more accurate when classifying and identifying.
  • Step 104 Perform classification processing on the two-dimensional feature value by using a classifier to obtain a discrimination result of the picture.
  • the convolutional neural network-based image discriminating method is sequentially connected by a convolution layer of a plurality of small windows (ie, the core of the convolution layer is small), so that the picture can be extracted better and faster.
  • the local features and the quick combination of these local features into advanced features can greatly improve the speed and efficiency of image recognition.
  • the image discriminating method and system based on the convolutional neural network according to the present invention uses the average pooling and the processing of the all-connected layer to make the final output of the image data into two features, thereby causing the classifier to perform classification and discrimination processing. Not only faster, but also more accurate.
  • the at least two concatenated layers connected in series comprise four convoluted layer C1 layers, C2 layers, C3 layers and C4 layers connected in sequence, and the core of the convolution layer
  • the size is: C1 layer core size is 3 ⁇ 3, C2 layer core size is 3 ⁇ 3, C3 layer core size is 5 ⁇ 5, and C4 layer core size is 5 ⁇ 5.
  • the number of steps of the four successively connected convolution layers is 1; the number of convolution kernels of the four convolutional layers is 96; the C1 layer and the C2 layer
  • the pad value is 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the step number of the convolution layer refers to the step size of each movement of the core of the convolution layer, and the pad value refers to whether a circle of data is added to participate in the operation around the input data, and the size of the pad value is added data. The number of laps. In this way, the processing efficiency and speed of the convolution layer can be further improved, thereby improving the efficiency of picture authentication.
  • the feature data extracted by the image is repeatedly subjected to dimensionality reduction and feature data of the feature data by at least one pooling layer and at least one convolution layer.
  • the step 102 of extracting and obtaining the feature data after the dimension reduction includes: passing the feature data extracted by the image through the pooled layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, and the convolution which are sequentially connected.
  • the convolution layer C5 has a kernel size of 5, a step number of 1, a pad value of 2, and a number of convolution kernels of 256; a kernel size of the convolutional layer C6, the convolutional layer C7, and the convolutional layer C8. All are 3, the number of steps is 1, the pad value is 1, and the number of convolution kernels is 384, 384, and 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer They are 2048, 2048, 2048, 2; and all connected layers are processed by dropout.
  • the node data can also be understood as the number of features.
  • the dropout method is to discard the remaining data by randomly opening a certain number of data, so that the over-fitting of the data can be effectively prevented, thereby improving the efficiency of the authentication.
  • FIG. 2 it is a schematic structural diagram of a convolutional neural network calculation model provided by the present invention.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, and the volume.
  • the processing of the layer C7, the convolution layer C8, the pooling layer P8, the fully-connected layer fc9, the fully-connected layer fc10, the fully-connected layer fc11, and the fully-connected layer fc12 is then connected to the classifier SVM for classification processing to obtain a picture. Identify the results.
  • All of the convolutional layers and the fully connected layer fc9, the fully connected layer fc10, and the fully connected layer fc11 are processed by the activation function LEAKY RELU, so that the data of the previous layer can be transferred to the next layer.
  • the activation function calculates a new output result by the last output data through an algorithm in the activation function, and uses the new output result as the input data of the next layer.
  • the invention makes it more suitable for the identification of the binary problem by selecting the classifier SVM (Support Vector Machine).
  • the activation function LEAKY RELU used in the present invention has a certain output value when the function value is less than zero, that is, the data of the part whose function value is less than zero can also participate in the training process, compared with the conventional activation function RELU.
  • the output value is multiplied by a coefficient a, which is preferably a fixed value.
  • all of the convolutional layers and all of the fully connected layers perform activation processing of data using an activation function LEAKY RELU.
  • the last fully connected base layer may not require an activation function. In this way, data transfer can be made more efficient.
  • the present invention prepares a 100-hour video of the positive and negative training samples, and intercepts 1.1 million pictures from the video, wherein the positive sample training picture is 500,000 and the negative sample training picture is 500,000. There are 100,000 test samples and 50,000 positive and negative samples.
  • the convolutional layer in the network is initialized with a Gaussian distribution with a standard deviation of 0.01.
  • the coefficient a parameter of the LEAKY RELU function is 0.01.
  • the parameters in the fully connected layer are initialized with a Gaussian distribution with a standard deviation of 0.002.
  • the dropout module has a parameter of 0.5.
  • the training process uses the back propagation algorithm (BP algorithm) to train and update the parameters. A total of 300,000 iterations are trained in the present invention.
  • BP algorithm back propagation algorithm
  • FIG. 3 it is a schematic structural diagram of an embodiment of a picture identification system based on a convolutional neural network according to the present invention.
  • the convolutional neural network based picture authentication system includes:
  • the data extraction module 201 is configured to input image data to be identified into at least two convolution layers connected in series to perform continuous feature extraction, obtain feature data after image extraction, and send the feature data extracted by the image to data dimensionality reduction.
  • Module 202 wherein, the core size of the at least two convolution layers are no more than 5 ⁇ 5;
  • the data dimension reduction module 202 is configured to receive the feature data extracted by the image extraction module 201, and perform the feature data of the image extraction through at least one pooling layer and at least one convolution layer.
  • the dimensionality reduction of the data and the extraction of the feature data are performed to obtain the feature data after the dimension reduction of the image, and the obtained feature data of the reduced image is sent to the full connection module 203; wherein the pooling layer adopts an average pool;
  • the full connection module 203 is configured to receive the feature data of the reduced dimension of the image sent by the feature dimension reduction module 202, and input the feature data of the reduced dimension of the image into at least one fully connected layer to obtain a two-dimensional feature of the image data. a value; the obtained two-dimensional feature value of the picture data is sent to the classification module 204;
  • the classification module 204 is configured to receive the two-dimensional feature value of the picture data sent by the full connection module 203, and classify the two-dimensional feature value by using a classifier to obtain a picture identification result.
  • the convolutional neural network-based image discriminating system completes the convolution of data by the data extraction module 201, and then extracts the features of the digital data, and then implements the feature by the data dimension reduction module 202.
  • the dimensionality reduction process obtains the two-dimensional feature value of the picture data through the full connection module 203, and finally the image data is identified by the classification module 204.
  • the convolutional neural network-based image discriminating system realizes effective extraction of feature data by using a convolution layer of a smaller kernel, which not only improves the efficiency and speed of image discrimination, but also effectively prevents over-fitting.
  • the data extraction module 201 includes: four convolution layer C1 layers, C2 layers, C3 layers, and C4 layers connected in sequence, and the core sizes of the convolution layers are respectively: C1
  • the size of the layer core is 3 ⁇ 3
  • the size of the C2 layer is 3 ⁇ 3
  • the size of the C3 layer is 5 ⁇ 5
  • the size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four successively connected convolution layers is 1; the number of convolution kernels of the four convolutional layers is 96; the C1 layer and the C2 layer The pad value is 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the data dimension reduction module 202 includes: a pooling layer P4, a convolution layer C5, a pooling layer P5, a convolution layer C6, a convolution layer C7, and a convolution layer which are sequentially connected.
  • the core size is 5, the number of steps is 1, the pad value is 2, and the number of convolution kernels is 256; the core sizes of the convolution layer C6, the convolution layer C7, and the convolution layer C8 are all 3 steps.
  • the number is 1, the pad value is 1, and the number of convolution kernels is 384, 384, and 256, respectively.
  • the fully connected module 203 includes: a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer is respectively It is 2048, 2048, 2048, 2; and all connected layers are processed by dropout.
  • the system includes a convolution layer C1, a convolution layer C2, a convolution layer C3, a convolution layer C4, a pooling layer P4, a convolution layer C5, and a pool which are sequentially connected.
  • the SVM is classified and processed to obtain the discrimination result of the picture.
  • all of the convolutional layers and all of the fully connected layers perform activation processing of data using an activation function LEAKY RELU.
  • an embodiment of the present invention further discloses an electronic device including at least one processor 810; and a memory 800 communicably connected to the at least one processor 810; wherein the memory 800 stores An instruction executed by the at least one processor 810, the instructions being executed by the at least one processor 810 to enable the at least one processor 810 to input picture data to be authenticated into at least two concatenated concatenations
  • the layer performs continuous extraction of features to obtain feature data after image extraction; and extracts the feature data extracted by the image through at least one pooling layer and at least one convolution
  • the layer performs dimension reduction of the feature data and extracts the feature data, and obtains feature data after the dimension reduction of the image; wherein the pooling layer adopts an average pooling; and the feature data of the reduced dimension of the image is input into at least one fully connected layer.
  • the two-dimensional feature value of the picture data is obtained; and the two-dimensional feature value is classified and processed by the classifier to obtain a picture identification result.
  • the electronic device also includes an input device 830 and an output device 840 that are electrically coupled to the memory 800 and the processor, the electrical connections preferably being connected by a bus.
  • the at least two concatenated layers connected in series comprise four convoluted layer C1 layers, C2 layers, C3 layers, and C4 layers connected in sequence, and the core of the convolution layer
  • the sizes are: the core size of the C1 layer is 3 ⁇ 3, the core size of the C2 layer is 3 ⁇ 3, the core size of the C3 layer is 5 ⁇ 5, and the nuclear size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four sequentially connected convolution layers is 1; the number of convolution kernels of the four convolution layers is 96; the C1 layer and the C2 The pad values of the layers are all 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain a picture drop.
  • the step of dimensioning the feature data includes: passing the extracted feature data through the pooled layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, the convolution layer C7, and the convolution layer.
  • the pooling layer P4, the pooling layer P5, and the pooling layer P8 have a kernel size of 3, a number of steps of 2, and a pad value of 0;
  • the convolution layer C5 The core size is 5, the number of steps is 1, the pad value is 2, and the number of convolution kernels is 256; the core sizes of the convolution layer C6, the convolution layer C7, and the convolution layer C8 are all 3 steps.
  • the number is 1, the pad value is 1, and the number of convolution kernels is 384, 384, and 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer They are 2048, 2048, 2048, 2; and all connected layers are processed by dropout.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, and the pool.
  • the SVM is classified and processed to obtain the discrimination result of the picture.
  • all of the convolution layers and all of the fully connected layers are The activation process of the data is performed using the activation function LEAKY RELU.
  • Embodiments of the present invention also disclose a non-volatile computer storage medium, wherein the storage medium stores the computer-executable instructions of computer-executable instructions that, when executed by an electronic device, enable an electronic device to be authenticated
  • the picture data is input into at least two concatenated layers connected in series to perform continuous feature extraction to obtain feature data after image extraction; and the feature data extracted by the picture is characterized by at least one pooling layer and at least one convolution layer
  • the dimensionality reduction of the data and the extraction of the feature data are performed to obtain the feature data after the dimensionality reduction of the image; wherein the pooling layer adopts the average pooling; and the feature data after the dimensionality reduction of the image is input into at least one fully connected layer,
  • the two-dimensional feature value of the picture data; the two-dimensional feature value is classified and processed by the classifier to obtain the identification result of the picture.
  • the at least two concatenated layers connected in series comprise four convoluted layer C1 layers, C2 layers, C3 layers and C4 layers connected in sequence, and the core of the convolution layer
  • the sizes are: the core size of the C1 layer is 3 ⁇ 3, the core size of the C2 layer is 3 ⁇ 3, the core size of the C3 layer is 5 ⁇ 5, and the nuclear size of the C4 layer is 5 ⁇ 5.
  • the number of steps of the four sequentially connected convolution layers is 1; the number of convolution kernels of the four convolution layers is 96; the C1 layer and the C2 The pad values of the layers are all 1, and the pad values of the C3 layer and the C4 layer are both 2.
  • the feature data extracted by the image is repeatedly subjected to dimension reduction and feature data extraction by at least one pooling layer and at least one convolution layer to obtain a picture drop.
  • the step of dimensioning the feature data includes: passing the extracted feature data through the pooled layer P4, the convolution layer C5, the pooling layer P5, the convolution layer C6, the convolution layer C7, and the convolution layer.
  • the pooling layer P4, the pooling layer P5, and the pooling layer P8 have a kernel size of 3, a number of steps of 2, and a pad value of 0;
  • the convolution layer C5 The core size is 5, the number of steps is 1, the pad value is 2, and the number of convolution kernels is 256; the core sizes of the convolution layer C6, the convolution layer C7, and the convolution layer C8 are all 3 steps.
  • the number is 1, the pad value is 1, and the number of convolution kernels is 384, 384, and 256, respectively.
  • the at least one fully connected layer is a fully connected layer fc9, a fully connected layer fc10, a fully connected layer fc11, and a fully connected layer fc12 connected in sequence; wherein the number of nodes of the fully connected layer They are 2048, 2048, 2048, 2; and all connected layers are processed by dropout.
  • the picture data to be identified passes through the convolution layer C1, the convolution layer C2, the convolution layer C3, the convolution layer C4, the pooling layer P4, the convolution layer C5, and the pool.
  • Layer P5 The processing of the convolutional layer C6, the convolutional layer C7, the convolutional layer C8, the pooling layer P8, the fully connected layer fc9, the fully connected layer fc10, the fully connected layer fc11, and the fully connected layer fc12 is then connected to the classifier SVM. Classification processing, to obtain the identification result of the picture.
  • all of the convolutional layers and all of the fully connected layers perform activation processing of data using an activation function LEAKY RELU.
  • Embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer
  • the computer is caused to perform the method described in the above embodiments.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the device is implemented in a flow or a flow chart The functions specified in a block or blocks of a flow and/or block diagram.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

L'invention concerne un procédé et un système d'identification d'images basés sur un réseau neuronal convolutif, ainsi qu'un dispositif électronique. Le procédé comporte les étapes consistant à: introduire des données d'une image dans au moins deux couches de convolution reliées en série en vue d'une extraction de caractéristiques pour obtenir des données de caractéristiques extraites, les tailles de noyaux des couches de convolution n'étant pas supérieures à 5×5; effectuer une réduction de dimensionnalité de données de caractéristiques et une extraction sur les données de caractéristiques extraites au moyen de couches de regroupement et des couches de convolution, pour obtenir des données de caractéristiques de dimension réduite, les couches de regroupement utilisant un regroupement de moyennes; introduire les données de caractéristiques de dimension réduite de l'image dans une couche entièrement connexe pour obtenir des valeurs de caractéristiques bidimensionnelles des données de l'image ; classifier les valeurs de caractéristiques bidimensionnelles au moyen d'un classificateur pour obtenir un résultat d'identification de l'image. L'invention concerne également un système d'identification d'images basé sur un réseau neuronal convolutif. Le procédé et le système d'identification d'images basés sur un réseau neuronal convolutif extraient des données de caractéristiques au moyen de couches de convolution dotées de plus petits noyaux, de façon à extraire mieux et rapidement des caractéristiques locales d'une image, améliorant ainsi la vitesse et le rendement de l'identification d'images.
PCT/CN2016/096031 2016-03-30 2016-08-19 Procédé et système d'identification d'images basés sur un réseau neuronal convolutif, et dispositif électronique WO2017166586A1 (fr)

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