Source | Global Artificial Intelligence (ID:aicapital)
Image recognition technology is an important technology in the information age, aimed at allowing computers to replace humans in processing large amounts of physical information. With the development of computer technology, human understanding of image recognition technology has deepened. The process of image recognition technology is divided into information acquisition, preprocessing, feature extraction and selection, classifier design, and classification decision. This article briefly analyzes the introduction of image recognition technology, its technical principles, and pattern recognition, and then introduces the image recognition technology of neural networks and nonlinear dimensionality reduction, as well as the applications of image recognition technology. It can be concluded that image processing technology has a wide range of applications, and human life cannot be separated from image recognition technology, making research on image recognition technology of great significance.
1. Introduction to Image Recognition Technology
Image recognition is an important field of artificial intelligence. The development of image recognition has gone through three stages: character recognition, digital image processing and recognition, and object recognition. Image recognition, as the name suggests, involves various processing and analysis of images to ultimately identify the target of study. Today, image recognition refers not only to human visual identification but also to recognition aided by computer technology. Although human recognition capabilities are powerful, they are insufficient to meet the demands of a rapidly developing society, leading to the emergence of computer-based image recognition technology. This is similar to studying biological cells, where relying solely on the naked eye is impractical, thus leading to the development of instruments such as microscopes for precise observation. When a field has inherent demands that existing technologies cannot meet, new technologies will emerge correspondingly. The emergence of image recognition technology is to enable computers to handle large amounts of physical information that humans cannot recognize or have very low recognition rates.
1.1 Principles of Image Recognition Technology
In fact, the principles behind image recognition technology are not very difficult; it is merely the complexity of the information to be processed that makes it challenging. Any processing technology of computers does not arise out of thin air; it is inspired by scholars from practical life and implemented through programming. The principles of computer image recognition technology are not fundamentally different from human image recognition; the only difference is that machines lack the influence of human sensory and visual discrepancies. Human image recognition is not solely based on the memory of the entire image stored in our minds; we rely on the inherent features of the images to categorize them and then recognize them based on the characteristics of each category, although we often do not realize this process. When we see an image, our brains quickly sense whether we have seen this image or a similar one before. In fact, there is a rapid recognition process that occurs between ‘seeing’ and ‘sensing’, which is somewhat similar to searching. During this process, our brains recognize based on the categories already stored in memory to check if there are any stored memories with the same or similar features as the image, thus recognizing whether we have seen the image before. The machine’s image recognition technology works similarly, identifying images by classifying and extracting important features while filtering out redundant information. The features extracted by the machine may sometimes be very obvious, while at other times they may be quite ordinary, which significantly affects the speed of machine recognition. In summary, in computer visual recognition, the content of the image is usually described using image features.
1.2 Pattern Recognition
Pattern recognition is an important component of artificial intelligence and information science. It refers to the analysis and processing of different forms of information representing things or phenomena to obtain descriptions, identifications, and classifications of those things or phenomena.
The image recognition technology of computers simulates the human image recognition process. Pattern recognition is essential in the image recognition process. Originally, pattern recognition was a basic intelligence of humans. However, with the development of computers and the rise of artificial intelligence, human pattern recognition has become insufficient for the needs of life, leading to the desire to use computers to replace or extend some of human mental labor. Thus, computer pattern recognition was born. Simply put, pattern recognition is the classification of data, closely linked to mathematics, with most of its concepts based on probability and statistics. Pattern recognition can be divided into three categories: statistical pattern recognition, syntactic pattern recognition, and fuzzy pattern recognition.
2. The Process of Image Recognition Technology
Since the principles of computer image recognition technology are the same as those of human image recognition, their processes are also quite similar. The process of image recognition technology consists of the following steps: information acquisition, preprocessing, feature extraction and selection, classifier design, and classification decision.
Information acquisition refers to converting light or sound information into electrical information through sensors. This means acquiring the basic information of the object of study and transforming it into information that machines can recognize using some method.
Preprocessing mainly refers to operations in image processing such as denoising, smoothing, and transformation to enhance the important features of the image.
Feature extraction and selection refer to the need for feature extraction and selection in pattern recognition. Simply put, the images we study are diverse, and to distinguish them using certain methods, we must rely on the inherent features of these images for recognition, and the process of obtaining these features is feature extraction. Not all features obtained during feature extraction may be useful for this recognition; thus, we need to extract useful features, which is feature selection. Feature extraction and selection are crucial technologies in the image recognition process, making understanding this step a key point in image recognition.
Classifier design refers to obtaining a recognition rule through training, allowing for the classification of features, thus enabling the image recognition technology to achieve a high recognition rate. Classification decision refers to classifying the recognized objects in the feature space to better identify which category the studied object belongs to.
3. Analysis of Image Recognition Technology
With the rapid development of computer technology and continuous scientific progress, image recognition technology has been applied in many fields. On February 15, 2015, Sina Technology released a news item stating: “Microsoft recently published a research paper on image recognition, revealing that in a benchmark test for image recognition, computer systems have surpassed human recognition capabilities. The human error rate in classifying images in the Image Net database is 5.1%, while this deep learning system from Microsoft Research can achieve an error rate of 4.94%.” From this news, we can see that image recognition technology is trending towards surpassing human capabilities in image recognition. This also indicates that the future of image recognition technology holds greater research significance and potential. Moreover, computers indeed possess advantages that humans cannot surpass in many aspects, which is why image recognition technology can bring more applications to human society.
3.1 Neural Network Image Recognition Technology
Neural network image recognition technology is a relatively new type of image recognition technology, which integrates neural network algorithms based on traditional image recognition methods. Here, the neural network refers to artificial neural networks, meaning that this neural network is not the actual neural network possessed by animals but is artificially generated by humans imitating animal neural networks. In neural network image recognition technology, a neural network image recognition model that integrates genetic algorithms and BP networks is very classic and has applications in many fields. In image recognition systems that utilize neural networks, image features are generally extracted first, and then the features of the images are mapped to the neural network for image classification. For instance, in automatic license plate recognition technology, when a car passes by, the detection device inherent to the car will sense it. At this point, the detection device will activate the image acquisition device to obtain images of the car from both the front and back. Once the images are acquired, they must be uploaded to a computer for storage for recognition. Finally, the license plate positioning module will extract license plate information, recognize the characters on the license plate, and display the final result. In the process of recognizing the characters on the license plate, template matching algorithms and artificial neural network algorithms are utilized.
3.2 Nonlinear Dimensionality Reduction Image Recognition Technology
Computer image recognition technology is an exceptionally high-dimensional recognition technology. Regardless of the resolution of the image itself, the data generated is often multidimensional, which poses significant challenges for computer recognition. To enable computers to have efficient recognition capabilities, the most direct and effective method is dimensionality reduction. Dimensionality reduction can be divided into linear and nonlinear dimensionality reduction. For example, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are common linear dimensionality reduction methods, characterized by their simplicity and ease of understanding. However, linear dimensionality reduction processes the entire data set as a whole, seeking the optimal low-dimensional projection of the entire data set. It has been verified that this linear dimensionality reduction strategy has high computational complexity and occupies relatively more time and space, thus leading to the emergence of nonlinear dimensionality reduction image recognition technology, which is an extremely effective nonlinear feature extraction method. This technology can discover the nonlinear structure of images and perform dimensionality reduction without damaging their intrinsic structure, allowing computer image recognition to be conducted at the lowest possible dimensionality, thereby improving recognition speed. For example, the dimensionality required for face image recognition systems is usually very high, and its complexity undoubtedly poses a significant ‘disaster’ for computers. Due to the uneven distribution of face images in high-dimensional space, humans can use nonlinear dimensionality reduction technology to obtain compactly distributed face images, thus enhancing the efficiency of face recognition technology.
3.3 Applications and Prospects of Image Recognition Technology
Computer image recognition technology has applications in many fields, including public safety, biology, industry, agriculture, transportation, and healthcare. For instance, license plate recognition systems in transportation; facial recognition technology and fingerprint recognition technology in public safety; seed recognition technology and food quality detection technology in agriculture; and electrocardiogram recognition technology in medicine. With the continuous development of computer technology, image recognition technology is also continually optimized, and its algorithms are constantly improving. Images are the primary source through which humans acquire and exchange information, so image recognition technology related to images is bound to be a future research focus. In the future, computer image recognition technology is likely to emerge in more fields, with limitless application prospects, making human life increasingly dependent on image recognition technology.
Although image recognition technology is a relatively new technology, its applications are already quite widespread. Moreover, image recognition technology continues to grow; as technology advances, human understanding of image recognition technology will deepen. In the future, image recognition technology will become more powerful and intelligently integrated into our lives, bringing significant applications to more fields of human society. In the information age of the 21st century, we cannot imagine what our lives would be like without image recognition technology.
