Automatic Vs Manual Image Annotation
Science and technology are the most significant achievement of human being. If you look twenty to thirty years back, you are easily able to analyze the tremendous difference. Nowadays the technology reached such a high peak that you can quickly call it a robotic age.
While talking about technology, it can open up how computers and machines assist in all kinds of sectors. Machine learning and artificial intelligence are now spreading their reach, and operating the process annotations plays a vital role.
Image Annotation
Machine learning and Artificial Intelligence gives computers the ability to behave like humans. The process of explaining image content to computer is called image annotation. When it comes to computer visionary, it always came to mind the vision of robots, automatic vehicle driving, detecting the very micro-organisms.
Image annotation is a time consuming process as huge volume of data needs to be annotated to train the model. The most popular and effective option is manual annotation. The annotation process is done by trained annotators. In recent days tools are developed to automate the process but it’s not 100% effective in comparison with manual annotation.
Different types of Image Annotation Tasks
There are various types of annotation to define an object. The most common and easy annotation task is bounding box. The bounding box technique is very effective in most of the machine learning processes. However there are specific requirements from Agrotech, health care industries which demands semantic segmentation annotation tasks. The following are some of the annotation techniques.
2. Semantic Segmentation
3. Landmarking technique
4. Masking technique
6. Polyline technique
7. Tracking technique
8. Transcription technique
Automatic Image Annotation
Automatic image annotation is the advanced and automated image annotation technique. It reduces the intervention of human work force thus saves lot of time and money. Currently the automatic annotation output cannot be used without manual alterations or quality checks. If the data label gets wrong, the machine will provide the whole inaccurate results. The automatic image Annotation can be used in certain annotation techniques only.
Manual Image Annotation
Even though manual annotation consumes time, demands huge work force and money, it is effective and accurate. The AI expert can decide the technique depending on the requirement and volume of data needs to be annotated. The bounding box or polygon techniques are more accessible because these are less time-consuming and easy to put on. The manual image annotation comes with fewer mistakes as compared to automatic image annotation.
Conclusion
The mode of annotation needs to be decided based on volume of data, timeline, requirement and budget. The preferred technique is still manual annotation because of high accuracy output. Even if someone chooses automatic annotation they need to plan for manual intervention to get accurate output. We at Infosearch provides both manual and automated annotation tasks. Our team is trained in performing 15+ types of annotation techniques.
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