Author: Zen and the Art of Computer Programming
1 Introduction
Humans have always been working hard to create new technological innovations, such as airplanes, cars, mobile phones, etc. In recent years, computer performance has continued to improve, so that we can search for the information we want in large amounts of data. But the field of image recognition is moving in a completely different direction. With the rapid development of machine learning and deep learning in recent years, the field of image recognition has become an important cornerstone in the field of artificial intelligence. This article will discuss the latest progress in the field of image recognition, mainly including: how to automatically identify objects from images; how to distinguish different scenes and environments; and how to achieve fusion recognition of multiple types of image data, etc.
2. Background introduction
In modern society, the popularization and application of the Internet have resulted in more and more information in people's lives, and it is increasingly processed digitally. At the same time, advances in sensor technology and image processing algorithms allow computers to extract effective information from large amounts of data. Compared with the past, which could only rely on human eyes to identify information, artificial intelligence can perform accurate identification by analyzing data when the amount of information is huge. Therefore, image recognition, as a hot topic of artificial intelligence, has extremely high research value.
Image recognition refers to the process of classifying, detecting and locating target objects in physical photos or videos captured by handheld cameras. Image recognition technology can be used to assist smart terminals in completing tasks, such as identifying pedestrians' movements on urban roads, identifying landing targets on aerospace vehicles, and identifying driver's behavioral characteristics in vehicles.
Image recognition can be divided into two major categories: supervised learning and unsupervised learning. Among them, supervised learning uses a trained model to classify, detect and identify certain specific objects or scenes. Unsupervised learning does not require labeling of training data, but only groups images through some clustering method.
At present, image recognition technology can be roughly divided into the following categories:
Classification type: Commonly used image classification techniques include pattern-based classification, graphics-based classification, statistics-based methods, etc. Among them, based on graphics