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Content-Adaptive Superpixel Segmentation Via Image Transformation

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Details

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages1505-1509
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

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

Publication forum classification

Field of science, Statistics Finland