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Understanding images is the key to creating machine learning models that can easily learn and understand the real world. Our image annotation service leads to better performing models.

One of the most popular uses of machine learning today is computer vision. It's in your phone camera and used to identify everything from people to text in photos. We know this is a crowded field, and we want to help. That's why our annotation experts are on hand to discuss how technology can make your business better.

With our Image Annotation Service, you can easily train your own AI models to more accurately recognize the objects captured in images. To provide an accurate dataset for computer vision projects, data scientists and ML engineers need to label images.

To create a novel labelled dataset for use in computer vision projects, data scientists and ML engineers have the choice between a variety of annotation types they can apply to images. Researchers will use an image markup tool to help with the actual labelling.

Boost your AI with our expert image annotation!

About Image Annotation

In image classification, Our goal is to simply identify which objects and other properties exist in an image without localizing them within the image. Image object detection, we go one step further to find the position (established by using bounding boxes) of individual objects within the image. In image segmentation, Our goal is to recognize and understand what's in the image at the pixel level. Every pixel in an image is assigned to at least one class, as opposed to object detection, where the bounding boxes of objects can overlap. This is also known as semantic segmentation.

For that we need to be acquainted with the different image annotation methods themselves. Let’s analyse the most common image annotation techniques.

1. Bounding Boxes

Bounding boxes are one of the most commonly used types of image annotation in all of computer vision, thanks in part to their versatility and simplicity. Bounding boxes enclose objects and assist the computer vision network in locating objects of interest.

2. Polygonal Segmentation

Another type of image annotation is polygonal segmentation, and the theory behind it is just an extension of the theory behind bounding boxes. Polygonal segmentation tells a computer vision system where to look for an object, but thanks to using complex polygons and not simply a box, the object’s location and boundaries can be determined with much greater accuracy.

3. Line Annotation

Line annotation involves the creation of lines and splines, which are used primarily to delineate boundaries between one part of an image and another. Line annotation is used when a region that needs to be annotated can be conceived of as a boundary, but it is too small or thin for a bounding box or other type of annotation to make sense.

4.Landmark Annotation

A fourth type of image annotation for computer vision systems is landmark annotation, sometimes referred to as dot annotation, owing to the fact that it involves the creation of dots/points across an image. Just a few dots can be used to label objects in images containing many small objects, but it is common for many dots to be joined together to represent the outline or skeleton of an object.

5. 3D Cuboids

3D cuboids are a powerful type of image annotation, similar to bounding boxes in that they distinguish where a classifier should look for objects. However, 3D cuboids have depth in addition to height and width.

6. Semantic Segmentation

Semantic segmentation is a form of image annotation that involves separating an image into different regions, assigning a label to every pixel in an image. Regions of an image that carry different semantic meanings/definitions are considered separate from other regions. For example, one portion of an image could be “sky”, while another could be “grass”. The key idea is that regions are defined based on semantic information, and that the image classifier gives a label to every pixel that comprises that region.

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