Timeline for What is the difference (if any) between semantic segmentation and multi-class, mutually exclusive classification?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 14, 2023 at 13:20 | vote | accept | tdMJN6B2JtUe | ||
| Jun 23, 2021 at 21:28 | comment | added | nbro | @brethvoice As far as I know, yes, it generally precludes a pixel from having more than one label, because, otherwise, it would be a multi-label classification problem. Of course, it may also be possible to formulate image segmentation so that you associate multiple labels to the same pixel, if that makes sense for your problem (not sure when that would be needed, though). | |
| Jun 23, 2021 at 11:59 | comment | added | tdMJN6B2JtUe | Here is what I am getting at: "objects of different classes have their pixels labelled differently" does not preclude a pixel from having more than one label simultaneously, does it? | |
| Jun 23, 2021 at 10:20 | comment | added | nbro | @brethvoice Maybe this description is what you are looking for: Object classification = sparse classification + typically only one main object in the image + each image is labelled with the only main object in the image. Image segmentation: dense classification (you classify pixels) + multiple objects in the image. In any case, the images above were supposed to intuitively convey the meaning of these tasks. | |
| Jun 23, 2021 at 10:18 | comment | added | nbro | @brethvoice If you ignore the "object detection" section (which was not part of your question, but I decided to add it because I think it can be useful) and the images, my answer is about 20 lines long. I don't know how to convey the definitions of these tasks in a shorter but still understandable way. | |
| Jun 22, 2021 at 14:18 | comment | added | tdMJN6B2JtUe | I was hoping for a straightforward answer, which is lacking. | |
| Jun 7, 2021 at 17:26 | history | answered | nbro | CC BY-SA 4.0 |