Therefore an image should be tagged with both: specific keywords and general descriptions of the image.įully automatic tagging systems will therefore always suffer of incomplete tagging. It is impossible to know, which tags they are using to search for specific images. But those are important to ensure a better retrieval rate when the image is searched by different users. Up to now, the automatic suggestions are more general keywords. Actually, tags, that are specific to the image (for example 'New York'), should definitely be added by the user.
PHOTOS TAGGER MANUAL
Manual tagging is time-consuming and means that the user has to type every single keyword. The user just finishes the tagging process and adds the new image to the collection. No user interaction is required for this step and the suggestions are already complete and correct. The user only retrieves ready to use tag suggestions. The tags will be processed and suggestions are calculated based on statistical analysis. This means that the new image is used to search for similar images in the collection. The suggestions are calculated based on the keywords of already indexed images that are visually similar to the input image. When a user uploads new images into your collection, the Keyword Suggester will automatically suggest tags. Therefore the proposed workflow is lean and fast. It is fully responsive and can be integrated tightly into current manual tagging approaches. The special feature is, that the Keyword Suggester can improve and narrow suggestions with each user interaction. Instead of using a pure manual or automatic image tagging workflow, we have implemented a hybrid: a keyword assistant called Keyword Suggester. Implementing an assisted tagging workflow Learn how our approach combines the best of both to implement a fast and accurate alternative. Manual tagging of images is accurate but expensive and automatic tagging is fast but still unreliable.