You must read this post if you wish to improve your content moderation by using an image labeling API.
What is the process of content moderation?
An online platform screens and monitors user-generated content based on the platform’s rules and standards to determine if it should be published there. To put it another way, when a user contributes content to a website, that content will go through a screening system (the moderating process) to verify that it follows to the website’s standards, is not inappropriate, harassing, or unlawful, and so on.
Content moderation is common in online systems that rely heavily on user-generated content, such as social networking platforms, online marketplaces, the sharing economy, dating sites, communities and forums, and so on. Some examples of content moderation include pre-moderation, post-moderation, reactive moderation, distributed moderation, and automated moderation.
What exactly is automated filter moderation?
Unwanted content is automatically identified and detected through the use of automated filter moderation, which is a set of rules. The filters (or rules) are effective at detecting information that cannot be misconstrued or is clearly fake.
Filters are also useful for dealing with sudden rule changes if the AI is not yet completely formed (Training takes some time and a quality data set). A classic illustration of this was when the Corona pandemic suddenly made masks and toilet paper an issue. Filters are therefore a helpful complement to the automatic tools in your moderating setup.
AI can help you automatically examine and approve user-generated text, audio, video, and image content to ensure it is appropriate for public consumption. AI may detect images or films that include damaging, insulting, racist, or threatening language and either restrict their release automatically or flag them for human review.
Utilize an API
Image labeling APIs allow you to recognize and extract information about things in an image from a variety of categories. The standard picture labeling model can recognize broad objects, places, activities, animal species, goods, and other things.
A bespoke image classification model can also be used to customize detection to a specific use case. In comparison to the user’s hand, the API can accomplish this quickly and correctly, while the human hand takes far longer. As a result, we recommend that you complete all of these activities utilizing the next APIs.
Image Tagging Content
Image Tagging Content API customers may rest certain that this program, which is constantly improving its capacity to recognise photographs, is simple to integrate and utilize in their enterprises. It provides a variety of automatic options for categorizing, organizing, and presenting your images based on groupings, colors, labels (which may be be adjusted), or customizable labels.
This implies that, while the tool provides a range of possibilities, you may also design new ones to match your specific requirements. This machine vision technology allows for the systematic utilization of visual characteristics by structuring recognition, classification, and comprehension procedures.
Clarifai
Another image recognition API is Clarifai. For evaluating visual data, it has 14 pre-built computer vision models. Also, it is simple to use. Clarifai only requires you to upload your media, and it will then provide predictions based on the model you are using.
DeepLobe
DeepLobe is another excellent API for picture recognition. It uses machine learning and artificial intelligence to analyse photos and videos. DeepLobe can detect, classify, and organize diverse products, persons, languages, and much more in addition to recognizing any improper content in your photos and videos.
It generates data rapidly and consistently by utilizing cutting-edge technologies. Image processing solutions help firms make better decisions faster and more efficiently by decreasing human error.