Image classification vs detection vs segmentation
What is Image classification, object detection or image segmentation and when to use each?
In computer vision, the most commonly used tasks are Image Classification, Object Detection, Semantic Segmentation and Instance Segmentation.
These computer vision tasks are used in a wide range of software to interpret and understand images and video, including software for geospatial analysis, predictive maintenance, digital asset management, content moderation, video analysis, to name a few.
|Task Type||Image Classification||Object Detection||Image Segmentation|
|What is in an image/video?||What objects are in the image/video and where? How many objects are there in the image?||Which pixels belong to which object?|
Image classification involves assigning a label or category to an image or video. The goal is to accurately classify an image into a specific pre-defined category or label. This is done by training a machine learning model with a dataset of images that have been labeled with their respective categories. Once the model has been trained, it can be used to predict the category or label of new, unseen images.
There are two common types of classification:
- Single label classification
- Multi-label classification
Single label classificaiton
With single-label classification, you can categorize data to a single class label. This is appropriate when you want to classify a single class, or when labels are mutually exclusive.
For example, an animal can be a cat, or a dog, but not both.
With multiple-label classification, you can categorize data into two or more class labels. If you are trying to recognize multiple features or attributes within an image, you can assign multiple labels.
For example, if you are trying to recognize the attributes of interior design photography, you may recognize the presence of beds, sofas, tables, chairs, fireplaces, and paintings. Using a multi-label classifier would help you recognize multiple of these features within a single image or video frame.
Popular Uses of Image Classification
Image classification has many applications, including identifying objects, people, and even diseases in medical images. Here are some popular uses of image classification:
- Digital asset management: Automate content tagging of media using AI models.
- Content moderation: Moderate user-generated content to remove harmful content like sexually explicit, illegal, or extreme content.
- Product categorization: Categorize an e-commerce product into its category - for example, flush-mount lights, pendant lighting, and sconces are all types of lighting.
In more advanced examples, classification models can be combined with other model types. For example, it is common to combine an object detection model with an image classification model. In this case, once a particular object is detected, you can classify that object into relevant subclasses.
Object detection distinguishes between instances of objects in an image or video, using bounding boxes to indicate their location within the pixel space. Object detection is useful when identifying particular objects in a scene, such as cars parked on the street, or people within a checkout line.
Detection models will often classify objects into higher-level groupings, and then classify each individual detection into sub-classes. For example, you may detect a vehicle, and then use a classifier to detect the make and model of each vehicle.
Popular Uses of Object Detection
Object detection is often used to scan digital images or videos to identify every object instance, separate them, and examine their characteristics. Here are some applications for object detection:
- People and vehicle detection and tracking: Recognize people and moving vehicles, and use tracking technology to assign unique instances to each detected object across multiple frames in a video.
- Demographics: Detect faces, and then classify each face based on gender, ethnicity, and age appearance.
- Satellite imagery analysis: Analyze satellite imagery to detect objects on the ground and detect changes across time, for example, airplanes at an airport, land development in geographical areas, or economic activity in urban areas.
Segmentation is another type of labeling where each pixel in an image is labeled with given concepts, providing pixel-by-pixel details for a given object. Image segmentation is considered more precise than other object detection methods because it labels individual pixels within an image.
There are two common types of segmentation:
- Instance Segmentation
- Semantic Segmentation
With semantic segmentation, all pixels of the same type of object or concept are grouped together. For example, in the above left photo, every pixel associated to a car is blue. For autonomous vehicles, you care less about detecting the individual instances of each car, but want to know where any car is.
Instance segmentation associates collections of pixels to each individual instance of an object. For example, in the above right photo, pixel groups can be seen for individual vehicles, people, trees, building facades, and even clouds. Use instance segmentation if you need to differentiate between unique instances of the same object category in an image.
Popular Uses of Image Segmentation
- Robotic vision: Robotic vision refers to a broad discipline of developing systems and algorithms that enable robots to perceive and understand the world visually. This includes applications in autonomous vehicles, industrial automation, medical robotics, and so on.
- Autonomous driving: Likely the most popular use case for segmentation. Autonomous vehicles use segmentation to identify instances of objects like other vehicles, people, and lane markings.
- Industrial visual inspection: Identifying defects on equipment or products on a manufacturing line is sometimes best done using segmentation. For example, this can be helpful for surface defect detection, like paint chips, rust, or rivet rash.
- Medical imaging: Segmentation can help medical professionals make more accurate diagnoses, including tumor detection, organ segmentation, blood vessel segmentation, and identifying neurological disorders via brain imaging.