Are you looking for a company to handle your image annotation needs? If you have images you need annotated for the purposes of training a neural network, there are many different companies that can provide image annotation services to you.
These image annotation companies include: Flatworld Solutions, Cogito, CloudFactory, Playment, Taskware, Gengo, and ImageAnnotation.ai
Image annotation is a complicated task. The best image annotation companies are able to deal with the complexity of image annotation in a timely and efficient manner, annotating images with precision and speed. Professional image annotators know how to deal with the many problems and considerations that come up when carrying out image annotation.
One of the first problems or considerations that professional image annotators must negotiate is deciding which type of annotation to use for which computer vision task. There are a variety of different image annotation types, and each annotation type has its own strengths and weaknesses.
The best image annotation companies know when they should (and shouldn’t) use the bounding box, which is a simple box drawn around an object in an image. The bounding box instructs the Convolutional Neural Network where within an image it should look to find an object at. The bounding box also contains information about the object within the box, applying a label to the object. However, sometimes the features of an object cannot easily be described with a box, and point annotation or line annotation is preferable instead.
Point annotation is done by labeling many individual points in an image, and it’s frequently used for tasks like face tracking, where a complex shape must be annotated. Meanwhile, line annotation is employed when the relevant features in an image have clear, definable edges. For example, line annotation can be used to distinguish lines on a highway for autonomous driving.
While bounding boxes assign classes to the objects within the bounding box, semantic segmentation is a type of annotation that assigns a label/class to every pixel within a region of interest. When semantic segmentation is done an image is broken up into multiple different regions based on the semantic meaning of that region, and then every pixel found in that region is given the same class. Semantic segmentation is useful when an image is divided in too many complex shapes, shapes too complex to be annotated with bounding boxes or line annotation. Examples of semantic regions in an image include grass, clouds, sidewalk, and trees.
While semantic segmentation contains much more detail than bounding boxes, instance segmentation gets even more complex than semantic segmentation. With instance segmentation, every individual instance of a class is distinguished from other instances of that class. So while in semantic segmentation all trees would be given the same label, in instance segmentation different trees would be given different labels.
As image annotation techniques grow in detail and become more complex, the amount of processing power needed to create and interpret them growth as well. It’s inefficient to annotate an image with the wrong type of annotation technique. You do not want to use an extremely complex and detailed form of annotation when a simpler form of annotation would work better. For this reason, knowing when to use what type of annotation is important.
The best image annotation companies know that the correct class must be chosen when assigning a label to the object being annotated. If an object is annotated with a bounding box, or another type of annotation, and then given the wrong label, the accuracy of the image classifier will be negatively impacted. The danger occurs mainly when there are multiple objects that look very similar but are part of different object classes. This is particularly prevalent when annotating objects in the fashion industry.
Many clothing items look similar to other items of clothing, yet they are classed as different objects because of subtle, yet important, differences. For instance, two jackets could be extremely similar, yet have zippers or pockets in different places. The best image annotation companies will train their image annotators to distinguish between these classes and make annotations with accuracy, quality, and speed.
Not only is accuracy, choosing the right label for the right object, a concern when making image annotations, it's also important that the annotations are of high quality. For instance, when a bounding box is drawn, the size and shape of the bounding box must not be too large or too small.
As bounding boxes are drawn they must be perfectly fit to the object they are enclosing. The bottom, top, left, and right edges of the object being annotated should just be touching sides of the bounding box. If the bounding box is smaller than this, it will be leaving parts of the object outside of the edges of the box, which leaves out critical features. If the bounding box is larger than this, unnecessary parts of the image will be included in the bounding box, and this can reduce classifier performance. Therefore, a bounding box must neither be too loose or too tight.
Bounding boxes can potentially overlap with one another without impacting of the accuracy of the image classifier. However, when there are many objects being annotated in the same image, the proliferation of bounding boxes can prove confusing to the image annotator. Skilled images annotators can deal with the large number of bounding boxes on a screen and ensure that each bounding box is correctly enclosing each object.
When it comes to doing semantic segmentation, it’s important that only the pixels which comprise the semantically distinguished region are assigned the desired label. If pixels in the image which aren’t part of the region are given the label for that region, the image classifier will become confused about the criteria needed to distinguish that region. Therefore, when semantic segmentation annotations are created, they must be done with pixel perfect detail.
The best image annotation companies also have a quality assurance process. Quality Assurance (QA) is usually the final portion of the image annotation process, and during QA the invitations are checked for accuracy, quality, and completeness by professionals. The different parts of the annotation, such as the bounding box, the class/label, and any other attributes associated with the annotation must be found complete and correct. If any mistakes are found, the QA member will correct for the mistake, creating a new tag and deleting the improper tag.
One of the benefits of maintaining a well-trained Quality Assurance team is that a dialogue can take place between the QA team and the image annotation team. If any mistakes are found, the mistakes can be corrected and the image imitators can be notified of the mistake so they’ll know what to watch out for in the future. If an image tagger has any questions, they can direct their questions to the QA team, who can help them sort out potential issue. This two-way communication street enables proper and efficient annotation of images, which helps ensure that an image data set is annotated with high quality and efficiency.