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Object labeling, though, may just be the latest spin on the image macro, Downer explained, a meme term that essentially identifies an image containing text. Image macros used to look a certain way: They featured animals and bold text, usually in the Impact typeface, with white letters and a black outline.
Image labeling gives you insight into the content of images. When you use the API, you get a list of the entities that were recognized: people, things, places, activities, and so on. Each label found comes with a score that indicates the confidence the ML model has in its relevance.
Label Connected Components Connected component labeling is the process of identifying the connected components in an image and assigning each one a unique label. You can specify the color map, background color, and how objects in the label matrix map to colors in the color map.
Annotation literally means to label a given data like image, video etc. For further references purposes. This is done by assigning some sort of keywords on the particular area of text, image etc. This is achieved by the annotations which can be from image, text, video, or audio.
Data labeling, in the context of machine learning, is the process of detecting and tagging data samples. The process can be manual but is usually performed or assisted by software.
Data labeling is the manual curation of data by humans on machine learning and AI applications. Roughly we call it supervised machine learning because computers need human supervision to get trained to execute tasks that are tricky for machines, but definitely easy for humans such as image recognition.
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