WiMi Develops Convolutional Neural Network Algorithm-Based Image Recognition System

The world is now a rapidly developing information age. Images have become a common way for people to express information closely related to life, and image recognition has become increasingly important. Image recognition enables computers to recognize information by receiving information, such as images and characters, and then processing the photos. The computer recognizes the processed images in this way. A convolutional neural network (CNN) is an accurate and effective method in image recognition.

According to the news, WiMi Hologram Cloud, Inc. (NASDAQ:WIMI) has developed an image recognition system based on the CNN algorithm. CNN is an efficient recognition algorithm developed based on an artificial neural network. WiMi applied CNN algorithm to image recognition technology, which shows apparent advantages. Compared with the traditional machine learning algorithm, CNN realizes the construction of features itself, thus breaking the bottleneck of the classification effect of the original artificial features. In addition, the structure of CNN is unique in that it can take two-dimensional images as an input layer so that some essential elements of images will not be lost, improving the correct rate of image recognition.

In CNN, the neurons in one layer are only connected to some neurons in the next layer. Instead, CNN uses a three-dimensional structure in which each group of neurons analyzes a specific region or “feature” of the image. CNN filters connections by proximity (analyzing pixels only for nearby pixels), allowing for a computationally sound training process. It consists of multiple stages of convolution and sampling, and then the extracted features are fed to the fully connected layer for the computation of classification results. The convolutional layer obtains the features of the image from the upper layer and the data on the unit nodes from each local area in the input layer, which need to cover the whole data set. The CNN can learn the image’s invariant features through feature extraction and feature mapping.

CNN-based image recognition systems perform well in image processing because of their multi-layer network structure and pooling operations and their ability to produce the best possible results with less training time. CNN generally consists of three or more neurons and is connected for training and inference. The convolutional layer is the core part of a CNN. The essence of convolution is to extract features from the data using the parameters of the convolution kernel and obtain the result by matrix dot product operation and summation operation. In the fully connected layer, linear stretching of the high-dimensional feature maps allows the high-dimensional feature maps to be transformed into one-dimensional vectors for classification or regression processing in the classifier. The activation function plays a crucial role in changing the mathematical relationship between the input and output data in the neural network. After adding the activation function, the output of the previous layer is first mapped by the activation function to obtain a nonlinear function, which can improve the learning and expression ability of the network.

The image recognition system based on the CNN algorithm developed by WiMi Hologram Cloud has the following advantages: First, it can extract features from multiple image datasets and select feature sets and features from datasets. Second, it can connect many small-scale units for learning and training. It can learn a series of essential parameters by understanding the relationship between different scales and obtain the optimal solution from them. Third, it can be trained by learning other parts of the data set so that more information can be extracted from the image data set and additional feature information can be better utilized. In many practical tasks, CNN uses pooling layers for network connectivity to obtain the desired features and eventually achieve target detection or target recognition; or share training results between different layers for tasks such as multiple classifications, regression, and image classification.

Image recognition technology is an important area of artificial intelligence. It has significant research and application value in many fields, such as navigation, resource analysis, environmental monitoring, and medical research. In the future, WiMi will continue to expand the application scenarios of the image recognition system with its developed CNN algorithm.