recognition vs classification，识别和分类的区别

recognition vs classification

The field of recognition or pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. However, pattern recognition is a more general problem that encompasses other types of output as well, for example, regression.

大意就是：

识别是对数据（比如图像）进行寻找规律、抽取特征，然后应用所得到的规律和特征实现某些目的（如分类、回归）的过程。所以分类只是识别的一个具体例子。

 

图像识别的定义

图像识别，是指利用计算机对图像进行处理、分析和理解，以识别各种不同模式的目标和对象的技术。

图像识别以图像的主要特征为基础的。每个图像都有它的特征,如字母A有个尖，P有个圈、而Y的中心有个锐角等。对图像识别时眼动的研究表明，视线总是集中在图像的主要特征上，也就是集中在图像轮廓曲度最大或轮廓方向突然改变的地方，这些地方的信息量最大。而且眼睛的扫描路线也总是依次从一个特征转到另一个特征上。由此可见，在图像识别过程中，知觉机制必须排除输入的多余信息,抽出关键的信息。同时,在大脑里必定有一个负责整合信息的机制，它能把分阶段获得的信息整理成一个完整的知觉映象。在人类图像识别系统中，对复杂图像的识别往往要通过不同层次的信息加工才能实现。（摘自百度百科）

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Image recognition is the ability of a computer powered camera to identify and detect objects or features in a digital image or video. It is a method for capturing, processing, examining, and sympathizing images.

Image recognition technology works by detecting salient regions, which are portions that contain the most information about the image or the object. It does this by isolating the most informative portions or features in a selected image and localizes them, while ignoring the rest of the features that may not be of much interest. （摘自Image Recognition – What is Image Recognition? | Sightcorp）

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Image recognition, a subcategory of Computer Vision and Artificial Intelligence, represents a set of methods for detecting and analyzing images to enable the automation of a specific task. It is a technology that is capable of identifying places, people, objects and many other types of elements within an image, and drawing conclusions from them by analyzing them.

Photo or video recognition can be performed at different degrees of accuracy, depending on the type of information or concept required. Indeed, a model or algorithm is capable of detecting a specific element, just as it can simply assign an image to a large category. 

So there are different “tasks” that image recognition can perform: 

• Classification. It is the identification of the “class”, i.e. the category to which an image belongs. An image can have only one class.  
• Tagging. It is also a classification task but with a higher degree of accuracy. It can recognize the presence of several concepts or objects within an image. One or more tags can therefore be assigned to a particular image.  
• Detection. This is necessary when you want to locate an object in an image. Once the object is located, a bounding box is placed around the object in question.   
• Segmentation. This is also a detection task. Segmentation can locate an element on an image to the nearest pixel. For some cases, it is necessary to be extremely precise, as for the development of autonomous cars.

（摘自Image Recognition : A Complete Guide - Deepomatic）

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人脸识别包含5个步骤：图像采集，人脸检测，图像预处理，特征提取，分析比对。