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WiMi Works on a CNN-based Image Feature Extraction Algorithm to Tap the Value of Image Data

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider,  announced the application of CNNs (convolutional neural networks) to image feature extraction and the development of the CNN-based image feature extraction algorithm.

CNN is a crucial deep-learning method that solves many complex pattern recognition problems and is widely used in image recognition, speech recognition, and natural language processing.

WiMi’s algorithm exploits the local connectivity and weight-sharing features of convolutional neural networks to automatically extract different image features of the same image by training with many other convolutional kernel parameters during image processing. The pooling operation can significantly reduce the number of training parameters, facilitate the feature map size, simplify the network model, and improve the training efficiency.

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The convolutional neural network consists of two alternating convolutional and pooling layers. The convolutional layer is responsible for extracting features from the input, while the pooling layer is responsible for integrating the features. The convolutional layer obtains local information from the image, the pooling layer significantly reduces the parameter magnitude, and the fully connected layer outputs the desired result.

First, the initial features are extracted by the convolution layer. The convolution layer, similar to a filter, is used to extract a specific initial feature from the image. After extensive training, the machine automatically adjusts the values of the convolution kernels and then convolves them with the image matrix to extract specific features from the image. The number of convolution kernels significantly impacts the initial feature extraction, but the time consumption increases accordingly. A pooling layer then extracts the main components. The main effect of the pooling layer is to reduce the number of training parameters, reduce the dimensionality of the feature vector output from the convolution layer and reduce overfitting, retain only the most helpful image information, and reduce the propagation of noise. In image processing problems, pooling layers can reduce the dimensionality of the feature map and introduce spatial invariance to image features, including stretching, rotation, and translation.

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The convolution and pooling layers work together to extract image features and significantly reduce the parameters introduced by the original image. Finally, the system applies fully connected layers to generate a classifier equal to the number of classes needed. The weight matrix is multiplied, offset values are added, and the parameters are optimized using an activation function and a gradient descent method. The fully connected layer is used for linear classification. In other words, it is a linear combination of the retrieved high-level feature vectors before being used to generate the final prediction.

Convolution kernels scan the entire image horizontally, vertically, and diagonally to generate feature maps. When the image is processed, each pixel in the output image uses a constrained receptive field, meaning that each pixel in the input image uses only a tiny part of the input image. By gradually expanding the receptive field of each successive convolutional layer, finer and more abstract information in the image can be obtained, and after several convolutional layers, an abstract representation of the image of different sizes is eventually obtained.

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If a computer can understand images as well as humans can, it can do many tasks that humans cannot even do. Making computers understand digital images is a key theme of current research in computer science. To a computer, a digital image is simply a matrix of numbers, so feature extraction algorithms are needed to help the computer understand the image.

WiMi’s image feature extraction algorithm based on the convolutional neural network has the translation and scale invariance for image processing, which can improve the accuracy of image feature extraction. It is essential to complete image recognition and image classification further. Convolutional neural network-based image feature extraction technology has been widely used in medical, security, autonomous driving, and other fields. WiMi will continue to expand the application of its image feature extraction algorithm in the future.

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