Content-Adaptive Superpixel Segmentation Via Image Transformation
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Image Processing (ICIP) |
| Publisher | IEEE |
| Pages | 1505-1509 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-5386-6249-6 |
| ISBN (Print) | 978-1-5386-6250-2 |
| DOIs | |
| Publication status | Published - Sep 2019 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Image Processing - Duration: 1 Jan 1900 → … |
Publication series
| Name | IEEE International Conference on Image Processing |
|---|---|
| ISSN (Print) | 1522-4880 |
| ISSN (Electronic) | 2381-8549 |
Conference
| Conference | IEEE International Conference on Image Processing |
|---|---|
| Period | 1/01/00 → … |
Abstract
We propose simple and efficient method that produces content-adaptive superpixels, i.e. smaller segments in content-dense areas and larger segments in content-sparse areas. Previous adaptive methods distribute superpixels over the image according to image content. In contrast, we transform the image itself to redistribute the content density uniformly across the image area. This transformation is guided by a significance map, which characterizes the ‘importance’ of each pixel. Arbitrary superpixel algorithm can be utilized to segment the transformed image into regular superpixels, providing a suitable representation for subsequent tasks. Regular superpixels in the transformed image induce content-adaptive superpixels in the original image facilitating the improved segmentation accuracy.
Keywords
- Image segmentation, Image edge detection, Optimization, Benchmark testing, Detectors, Measurement, Task analysis, Superpixel, image segmentation