Photo annotation refers to the process of labeling or tagging images with metadata or specific information that describes the content of the image. This can involve identifying and marking various elements within the image, such as objects, people, or actions. Photo annotation is commonly used in machine learning and artificial intelligence, particularly in computer vision tasks, to train models to recognize objects, classify images, or understand visual scenes.
There are various types of photo annotation techniques:
- Bounding Boxes: Rectangular boxes drawn around objects to define their location.
- Polygon Annotation: Custom shapes that closely outline the boundaries of objects.
- Semantic Segmentation: Every pixel in the image is labeled according to the object it belongs to.
- Key Point Annotation: Identifying specific points of interest (e.g., facial landmarks or body joints).
- 3D Cuboids: Annotating objects in three-dimensional space for depth information.
- Image Classification: Labeling the entire image with a tag representing its overall content.
This process is critical for training AI models to make accurate predictions in tasks like object detection, image classification, and facial recognition.
