What is image recognition?
Image recognition is a mechanism for identifying and classifying objects in images into specific categories, based on the way humans recognize objects in different sets of images.
How does image recognition work for humans?
When we see an object or image, we, as humans, know exactly what it is immediately. People classify everything they see into different kinds of categories based on recognized attributes, and even if we don't know exactly what the object is, we can usually compare it to objects of different categories we've seen in the past, and based on Its properties classify it. Let's take the example of an animal we don't know. Even if we can't clearly identify what animal it is, we'll still be able to identify it as an animal.
People give little thought to what they are looking at and how to recognize objects, it happens entirely subconsciously. People don't always pay attention to everything around them. Our brains have been trained to easily recognize objects based on our previous experience. We do have a remarkable ability to reason: when we see something similar to an object we have seen before, we are able to infer that it belongs to a certain category of items. We don't necessarily need to look at every part of an image to recognize objects in it. As soon as you see part of an item you recognize, you know what it is. We often use color and contrast to identify items.
For humans, most image recognition is subconscious. But when it comes to machine image recognition, the situation is much more complicated.
How does image recognition work with machines?
Machines can only recognize classes of objects that we program into them. They cannot naturally understand and recognize what they see. If a machine is programmed to recognize one class of images, it will not be able to recognize anything other than the program. The machine will only be able to specify whether an object present in a set of images corresponds to a class or not.
Train Image Recognition Using Machine Learning
The goal of image recognition is to identify, label and classify detected objects of different classes. Object or image recognition is a whole process involving various traditional computer vision tasks:
Image Classification: Label images and create categories.
Object Localization: Identify the location of an object in an image by enclosing the object with a bounding box.
Object Detection: Determine the presence of an object with the help of a bounding box and classify it into the class it belongs to.
Object Segmentation: Differentiate various elements. Identify and locate each item in the picture. Segmentation does not use a bounding box, but instead highlights the outlines of objects in the image.
Over the past few years, this computer vision task has achieved great success, largely due to the application of machine learning.
Different Application Areas of Machine Learning Image Recognition
Computer vision and recognition are all around us these days. From unlocking your phone with your face in the morning to entering a mall to shop. Many different industries have decided to implement AI in their processes.
face recognition
Face recognition is an important recognition application. Many companies use it to detect different faces simultaneously, for example, to know how many people are in an image. Police and security forces can use facial recognition to identify criminals or victims. Face recognition involves gender detection, emotion estimation, age estimation, etc.
The need for businesses to identify these characteristics is easy to understand. It allows them to identify exactly who their customers are. In this way, a fashion house can realize that 80% of its customers are women, the average age is between 30 and 45 years old, and customers do not seem to appreciate the goods in the store. Looking at the green dress, their facial expressions are often disappointed. Acknowledging all these details is necessary for them to understand their goals and adapt their communications in the future.
health and medicine
Treating patients can be challenging, and sometimes a tiny factor can be missed during the examination, causing medical staff to provide the wrong treatment. To prevent this from happening, healthcare systems began analyzing images taken during treatment. X-rays, radios, scans, all of these image materials can use image recognition to detect individual changes from one point to another. Detect the progression of tumors, viruses, the appearance of venous or arterial abnormalities, and more.
Smart Agriculture
The daily life of a farmer is anything but easy. In order to continue to take good care of their animals and plantations, they need to monitor them.
Monitoring their animals has become a comfortable way for farmers to observe their cattle. With cameras equipped with motion sensors and image detection programs, they are able to ensure that all their animals are in good health. They can also monitor animal births. Farmers can easily find out if a cow is having trouble giving birth to her calves. They could intervene quickly to help the animals give birth, preventing the potential death of both animals.
To see if the plants in the field are healthy, farmers can program image recognition to detect the presence of disease on the plants and respond accordingly.
security and safety
安保和安全是当今社会的两大关注点。借助图像识别和检测，可以更轻松地识别罪犯或受害者，甚至武器。例如，在机场，安全至关重要。安全人员在人工智能的帮助下，可以在屏幕前非常迅速地发现危险。当一件行李无人看管时，看守人员可以立即与现场人员取得联系，快速寻找失主。出示护照时，会识别个人的指纹和面部，以确保它们与原始文件匹配。
保险公司也在使用人脸识别技术。当有人提出关于抢劫的投诉并要求保险公司赔偿时。后者经常要求受害者提供视频片段或监控图像，以证明重罪确实发生了。这就可以利用人脸识别对罪犯进行追捕。
电子商务
自COVID-19大流行开始以来，在线商店正在经历繁荣。他们提出的最新进展之一是图像识别，以更好地为客户服务。许多平台现在能够识别他们的在线购物者最喜欢的产品，并根据他们之前看过的内容向他们推荐要购买的新商品。
另一方面，一些新的应用程序建议他们的用户只需拍摄他们在街上遇到的人身上发现的物品的照片，以便找到有类似或相同物品可供购买的商店。
过去几十年在人工智能和图片识别领域已经取得了巨大的进步。
景联文科技为全球数千家人工智能从业公司和高校科研机构提供AI数据采集、数据标注、数据集产品、假指纹采集和指纹防伪算法服务。景联文始终践行“做全球AI行业客户的数据参谋”的企业使命，助力人工智能技术加速数字经济相关产业质量变革、动力变革与效率变革，赋能传统产业智能化转型升级。