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

Rhea-AI Summary

On March 17, 2023, WiMi Hologram Cloud (NASDAQ: WIMI) announced the development of a CNN-based image feature extraction algorithm that enhances image recognition through convolutional neural networks (CNNs). This algorithm leverages local connectivity and weight-sharing to improve feature extraction efficiency while reducing training parameters, dimensionality, and noise propagation. The technology is applicable in various fields, including medical, security, and autonomous driving. WiMi aims to broaden the algorithm's applications, contributing to advancements in the AR industry.

Positive

• Development of a CNN-based image feature extraction algorithm that improves image recognition capabilities.
• Algorithm reduces training parameters and dimensionality, enhancing computational efficiency.
• Potential applications span across various industries, including medical and autonomous driving.

Negative

• None.

BEIJING, March 17, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today 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.

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.

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.

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.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI), whose commercial operations began in 2015, is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement, except as required under applicable laws.

 

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SOURCE WiMi Hologram Cloud Inc.

FAQ

What is the purpose of WiMi's new CNN-based image feature extraction algorithm?

The algorithm aims to improve image recognition and classification capabilities through advanced feature extraction.

When did WiMi announce the development of its new image feature extraction algorithm?

WiMi announced the algorithm on March 17, 2023.

What industries can benefit from WiMi's new technology?

The technology can benefit industries such as medical, security, and autonomous driving.

What are convolutional neural networks and how are they used by WiMi?

Convolutional neural networks (CNNs) are deep learning methods used for image feature extraction, enabling better pattern recognition.