AI Visual Tagging Introduction

AI Visual Tagging systems typically work by first processing an image through convolutional neural networks trained on large datasets of labeled images. These networks extract visual features and patterns from the pixels to identify objects, scenes, faces, text, and other elements. The system then matches these identified elements to a predefined taxonomy or vocabulary of tags. Some systems can also generate natural language captions or descriptions. More advanced AI tagging incorporates object detection to identify and locate multiple objects within an image. The tags and metadata are then associated with the image file, often stored in standard formats like EXIF or XMP. Many systems allow customization of the tag vocabulary and threshold settings to fit specific use cases. Some can also learn and improve their tagging accuracy over time through user feedback and additional training.