Computer vision is an important branch of artificial intelligence, which aims to build computer systems that can understand and process visual information such as images and videos. In the field of computer vision, image classification, image recognition and object detection are three important tasks.
1. Image classification
Image classification is one of the most basic tasks in the field of computer vision. Its purpose is to classify an image into a predefined category. For example, classify a picture of a cat into the category "cat". Generally, image classification refers to single-label classification, that is, each image belongs to only one category.
Image classification is a supervised learning process that usually consists of two phases: training and testing. In the training phase, the algorithm uses labeled images as input, and generates a classifier by learning the relationship between image features and category labels. During the testing phase, the algorithm uses the trained classifier to classify new images.
Common image classification algorithms include traditional machine learning algorithms and deep learning algorithms. In traditional machine learning algorithms, support vector machines (SVM), decision trees, random forests and other algorithms can be used to solve image classification problems. Among deep learning algorithms, convolutional neural network (CNN) is one of the most popular algorithms at present. CNN extracts image features through convolutional and pooling layers, and classifies through fully connected layers.
2. Image recognition
Image recognition is to recognize objects in an image, that is, to mark and classify each object that appears in the image. Unlike image classification, image recognition tasks require distinguishing and classifying each object, rather than classifying the entire image. For example, multiple objects such as cats, dogs, cars, etc. are recognized in one image. Image recognition usually refers to multi-label classification, that is, each image may belong to multiple categories.
Image recognition is a more complex task than image classification, and it relies on algorithms such as object detection, semantic segmentation, and instance segmentation. Object detection refers to locating and marking the position and size of objects in the image, semantic segmentation refers to marking each pixel in the image as belonging to which category, and instance segmentation refers to marking each pixel in the image as belonging to which category object.
Common image recognition algorithms include region-based methods, fully convolutional network (FCN), U-Net and other algorithms. Among them, the region-based method usually extracts the candidate frame from the region in the image, and then classifies and locates the candidate frame. FCN and U-Net implement pixel-level classification of images through convolutional neural networks.
3. Target detection
Object detection is to detect and recognize multiple objects in an image and give their location information. Different from image recognition, target detection needs to locate the object, that is, to give the position and size of the object in the image. For example, multiple pedestrians, vehicles and other objects are detected and located in a street view image.
Object detection usually consists of two tasks, object localization and object classification. Target positioning refers to accurately locating the position and size of the target in the image, while target classification is to classify the located target.
Common target detection algorithms include region-based methods, single-stage detection methods, two-stage detection methods, and so on. Region-based methods usually use candidate frame extraction and classification methods, such as RCNN, Fast RCNN, Faster RCNN, etc. Single-stage detection methods refer to predicting the location and category of objects directly from images, such as YOLO, SSD, etc. The two-stage detection method divides the target detection task into two stages, such as RPN+Fast RCNN, Mask RCNN, etc.
4. Relationship and difference
Image classification, image recognition, and target detection all belong to image analysis tasks in the field of computer vision. The relationship and differences between them are as follows:
relation
Image classification, image recognition, and object detection all extract useful information from an image and classify or locate it. They both require the recognition and classification of objects in images, but the tasks differ in difficulty and complexity. Image classification is the most basic task. It only needs to classify the entire image into a certain category; image recognition needs to mark and classify each object appearing in the image; target detection needs to detect and locate multiple objects in the image, and give their location information.
the difference
(1) Task difficulty and complexity are different
The image classification task is relatively simple, and only needs to classify the entire image into a certain category. The image recognition task needs to label and classify each object appearing in the image, which is more complex than the image classification task. The target detection task needs to detect and locate multiple objects in the image, and give their location information, which is more difficult than the image recognition task.
(2) The output results are different
The output of an image classification task is the category to which an image belongs. The output of image recognition tasks is the label and category of each object that appears in an image. The output of the object detection task is the location information and categories of multiple objects appearing in an image.
(3) Algorithms and models are different
Image classification tasks usually use models such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. Image recognition tasks usually use algorithms and models such as object detection, semantic segmentation, and instance segmentation. Object detection tasks usually use algorithms and models such as region-based methods and single-stage detection methods.
5. Application Scenarios
Image classification, image recognition, and object detection are widely used in many fields. For example, in the field of security, object detection can be used to identify and locate dangerous objects or suspicious persons; in the medical field, image recognition can be used to automatically diagnose medical images; in the field of automatic driving, object detection can be used to identify and Locate other vehicles and pedestrians on the road.
In short, image classification, image recognition and target detection are three important tasks in the field of computer vision. There is a slight relationship between them, but there are also great differences. In practical applications, appropriate tasks and algorithms need to be selected according to specific scenarios and requirements.