Image Annotation Service is nothing but application of knowledge about pictures. We use it to tag an image or extract its data. It has been generally accepted in the image processing community that image tag and image data mining share some principles and concepts.
Even if they are different terminology, these concepts are used together in many applications to tag, organize and search images. Let us discuss some of the tips for applying image classification services and in particular use KPI in practice.
DataAnnotation can be defined as a process that identifies and assigns labels to an image using high-quality data sets. The basic idea behind this concept is to create a knowledge base that can be used for making classifications. Image classification techniques may involve fuzzy logic, logistic regression, fuzzy logic or a combination of fuzzy logic and logistic regression.
Semantic web is a framework based on traditional notions of internet research and application research which aims to build a knowledge base around natural language concepts. Image classification may be defined as a tool to create and manage high-quality data sets from diverse sources.
Image Annotation is a subset of knowledge discovery/meta-linguistic models for recognizing and classifying objects. Image classification is a framework of artificial intelligence ( AI ) research. It can be defined as a collection of techniques for the supervised extraction of information from a large volume of unsupervised sources, typically text. Image classification is a powerful key for organizing huge unsupervised data sets and for generating high quality results from linear and logistic models of association.
The main goal of an image Annotation service is to deliver high quality results from a wide range of image processing tools. Image classification techniques are highly useful in applications requiring the use of large amounts of data, including image libelling, item recognition, object identification, face recognition, video conferencing, and speech recognition.
One of the most important characteristics of an effective Image Classification tool is the ability to make rapid and consistent predictions. Although machine learning is widely used for Image classification, many experts believe that good Image classification methods are largely learned through supervised and controlled training data.
A visual image labelling tool must have capabilities to automatically extract edges, shapes, and patterns from large numbers of unlabelled pictures. In addition, the labelling tool must be able to associate each shape with one of its associated keywords.
Image classification systems often utilize fuzzy logic, neural networks, or feature decomposition to achieve these goals. In short, the labelling tool must perform well with unsupervised and supervised training data, while providing excellent error and confidence management on supervised data.
As an Image classification or Annotation service provider, you should provide your clients with an easy way to visualize their results. By crowdsourcing your analysis tasks, you allow those who need to do so the opportunity to contribute to your business.
Image crowdsourcing allows your clients to quickly obtain and use image labels, allowing them to make their own corrections and understand their results in much more depth than if they were to perform this analysis by hand. Crowdsourcing your work allows you to focus your time and attention on the tasks that generate the most revenue, and it can greatly reduce the costs associated with running an Image classification or Annotation service.