Challenges in Image Annotation

Fueled by advancements and breakthroughs in Computer Vision, it is now possible to visualize, detect and track anything in real-time. Enormous amounts of images are fed into deep learning architecture, which was inspired by the human brain, in order to comprehend the features of a subject. It requires a significant amount of computation to extract features from such a large collection of images and videos. This might result in a longer training time for the network. Nevertheless, there are some advanced algorithms designed specifically to detect and identify items in real-time with the highest degree of accuracy with minimal training time, such as the YOLO algorithm. The only requirement for precision is an annotated dataset with appropriately tagged objects. Image Annotation tools provide solution to this problem by labelling objects in a dataset of images. This functions as the fundamental catalyst in bringing computer vision to life. However, there are still challenges with using image annotation tools e.g.

  • Sluggish – The procedure is extremely slow. Since there are millions of photos in the datasets required to train the neural network, it would take a long time to annotate every single one of them. In reality, great caution must be exercised when annotating in the case of instance segmentation, when each pixel of each object is intended to be segregated from others, which further extends the annotation time.
  • Finding a suitable Annotation Tool – It is impractical to manually annotate millions of images because the process is iterative and takes a long time. It is possible to prepare a well-labeled dataset for supervised learning by using various image annotation tools and platforms. With multiple types of annotations and features, there are different tools that can be used for different annotations but the challenge lies in choosing the most appropriate tool.
  • Cost – A single dataset containing millions of images necessitates annotations by more individuals in a shorter amount of time, thus increasing the cost.
  • Accuracy – Despite the fact that there are various annotation programs and some of them are fully automated, professional oversight is still necessary. To ensure the accuracy of annotations, a balance must be struck between the manual and automatic portions.
  • Data Security and Privacy– There are numerous online annotation platforms, however, they all require that the dataset being annotated be uploaded first. In those circumstances, maintaining cyber security has emerged as one of the top priorities.

When a model is attempting to address a problem in a novel field or domain, annotated data is very crucial, but creating annotated data can be difficult. But there are numerous approaches to overcome these obstacles to a large extent.

Some annotation and labelling strategies

If carefully followed, a few frameworks that assure quality and accuracy can produce a rich dataset that the deep neural network can use as eyes and interpreters to make decisions.

  • Labelling Format – Understanding the issue statement before beginning any annotations is the first step. Depending on the type of annotations being done—semantic or for classification—this will eventually help us select the best tool for the job. The objects can also be described in a variety of ways, such as bounding boxes, polygons, segments, and key points.
  • Precising the number of classes – Every model is trained for a particular task, and the collected dataset images should include variances and occlusions to elevate the effectiveness of predicting an actual object of the same class. These photographs could also include a lot of other things that aren’t related to the use case. Therefore, it should be quite clear which objects need to be identified, which classes they belong to, and how to annotate those objects. The rest can be considered background. You will also receive the further advantage of avoiding the needless computation costs incurred by those objects in the model.
  • Users Interaction– The primary goal is to annotate the datasets according to the stated categories in the shortest amount of time without sacrificing accuracy, and that can be achieved on the basis of the number of classes per image. The browsing method of annotation is better when there are few classes and many occurrences, where each detail is manually described, whereas if there are many classes in an image, a staging approach can save a lot of time since we can include a number of people in the process and they will then select the relevant images of a particular object from the many that are available.
  • Labelling Hierarchy – Vocabulary is one of the most important aspects of item labeling. The annotations should be labeled using established taxonomy presentation if the annotations are to be done for limited classes, like all dog breeds should be defined individually but in the context of dogs. This is particularly true if segmentation use cases are to be annotated. However, due to the sheer number of classes and the complexity of the semantic relationships between these classes, an ontology approach should be preferred. For example, you can classify all dog breeds as dogs, or you can keep synonyms and group them into one category.
  • Right Tool Selection– Once you know the details about the types of annotations you need, the number of classes, and the format your neural network needs, choosing the right annotation tools becomes the most important factor. Of course, not every image in a large dataset can be annotated manually, but what level of automation you choose is very important. The most effective approach is to split the images into batches and have manual annotation by field experts in the first few batches. Semi-automatic annotation can then be used for the next batches, but verification of the correct labels is left to the field experts. For datasets with relatively basic images and a small number of classes, a fully automatic annotation tool can be employed.
  • Use Image Labeling Aiding Tools– Many assistive technologies have been developed to minimize the time and cost annotators spend annotating images. A naive approach would be to suggest a relative label for each selected object, but since the suggested labels correspond to the frequencies of the objects in the dataset or the vocabulary follows the ontology, it is important to validate the results.

Another excellent aspect of annotating software is magic pixels, which combine together pixels that are contextually alike and generates a boundary, resulting in the creation of a recognizable object. Intelligent scissors that predict boundaries for an object, which the annotator then perfects, is another helpful technique.

  • Synthetic Datasets – Actual photos may not exist or exist in very limited quantities in some situations. For example, footage of an accident is very important for making a self-driving car, but without that footage, it would be impossible to create such a scene in real life. In such a situation, a model can be trained with the help of synthetic images that are similar but not identical to real-world images.
  • Skilled Annotators – In order to annotate objects effectively and flawlessly, training might be given to the annotators. Field experts can explore all the features of the annotating tool to be followed and provide the annotators with a hands-on demonstration while annotating several images in their presence.

Computer vision has seemingly endless real-world applications, ranging from simplistic image classification to very complex ones such as unmanned aerial vehicles. Annotating the images can be a challenge, but selecting the right annotation tool and utilizing various strategies discussed will result in high-quality and human-verifiable datasets.

Written By:
Parul Chutani