Success in creating AI would be the biggest event in human history… – Stephen Hawking.
Like a new gym enthusiast flexing his muscles too early or with breakthroughs such as self-driving cars, we tend to believe that Artificial Intelligence (AI) has already arrived and viable.
Cut to reality; we are still at the starting line.
AI teaches machines to be more intelligent, but it is still a toddler who needs constant supervision or—in the case of technology—loads of data to understand everything. We all know teaching involves a great degree of subject knowledge, and we can’t get insights unless we understand what we are staring at.
Data annotations are baby steps toward AI goals and are diversified into text annotations, image annotations, and video annotations. If you are new to this topic, hopefully this blog will throw some light on the task of image annotations.
Our databases are overcrowded with unstructured data that need to be simplified before being fed into machines to help them make sense of it.
Looking at a busy marketplace with our naked eyes or through a camera may not be much different for us humans. We can easily differentiate between a uniformed policeman and a security man at the mall. But computer vision needs anatomic detection, boxing, labeling, tagging, a coloring schema, and more to do the same.
Or another example: imagine a surgical robot scratching its metallic head, wondering what instrument to use to cut through muscle tissue − a scalpel or a forceps? Certainly, for a medical procedure like this, it is essential to train the AI accurately.
Annotating images is an art, and very few have mastered this art form. Several techniques are employed here, such as bounding boxes, 3D cuboids, polygons, line segmentation, semantic segmentation, landmark segmentation, pixel manipulation, heat maps, to name a few.
Image annotation is much like taking a hard copy of a picture and labeling the objects in the picture with a marking pen. It has applications for self-driving cars, crewless aerial vehicles, tumor detection, determining healthy food choices, and even identifying a potential terrorist with a weapon from an unsuspecting crowd.
But it is not easy; it is believed that we need at least 5000 positives per class to spoon feed the machines to learn.
Image Annotation Tools
You can find several ineffective image-annotating tools in the market that are fairly inexpensive with less than impressive results. As we identified earlier, machine learning is still at an early stage of development to be left to auto labeling. Auto labeling lacks nuance and creativity, and iterations may be too costly without reliability. You could end up in a mess and misspent dollars. You may have to go back to the storyboard and start afresh.
Humans versus Tools
There’s broad daylight between human labeling and auto-labeling.
Image annotations are not just boxing irregular shapes with polygons and randomly tagging the objects with alphanumerical values. It involves precise and unbiased methodologies where automated tools fall short.
One should opt for human-powered annotations when the goal is to achieve high accuracy, improved precision, and enhanced user experience. These image annotations help artificial neural networks with simple, trainable mathematical units to solve complicated tasks. Appropriate semantic labeling becomes crucial when you know that there could be corrections later. If done right, industry experts can pick any particular label easily to modify and iron out minor deficiencies that could have crept up inadvertently.
Humans are unbiased and will look into various factors before creating customized data sets that store suitable and relevant information in the database. Medical image annotations can in some cases be remarkably sensitive to be left to a machine. For example, a small bleeding point missed out in the retina can make a huge difference in a retinopathy diagnosis. Pixel-level image annotations help the machines simulate real-time environments and equip themselves with pertinent information to face the ultimate challenge of making accurate predictions.
Image Annotation Services
Sensitive projects like those involving medical engineering with complex algorithms need better image recognition and error-free labeling. These image annotations need large data sets, better organization, and more accuracy to give you the perfect results.
Whatever industry you belong to, if you want your AI/ML models to work for you, you must feed them with data that is validated, laser-sharp images boxed appropriately with detailed labeling and tagging. In the case of a surgical robot, once you have fed authentic images with detailed, pixel-focused, legitimate labeling, you will find it works exponentially better, without any confusion.
Make It Work with PreludeSys
When it comes to critical AI and ML, it is more than reasonable to outsource massive image annotation projects to a third-party provider who offers precision services with resourceful human capital. Outsourcing makes perfect business sense to save time and money, especially when accuracy and yields are critical.
The PreludeSys Edge
- Industry specific datasets
- Accurate annotations
- Quick turnaround time
- Easy on the wallet—a huge cost savings
- A-listers in the clientele
- Product modification services
Make your annotation experience meaningful. Call our team now to discuss your project.