Learning to realign hierarchy for image segmentation
| Autor: | Silvio Jamil Ferzoli Guimarães, Milena M. Adão, Zenilton Kleber Gonçalves do Patrocínio |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Hierarchy (mathematics) business.industry Computer science Pattern recognition 02 engineering and technology Image segmentation Object (computer science) 01 natural sciences Image (mathematics) Random forest Set (abstract data type) Artificial Intelligence 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence 010306 general physics business Software |
| Zdroj: | Pattern Recognition Letters. 133:287-294 |
| ISSN: | 0167-8655 |
| Popis: | A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects (or even parts of the same object) may appear at different scales due to their size differences or to their distinct distances from the camera. One possible solution to cope with that is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of regression models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation and a new assessment strategy considering all user-defined segmentations that exist in the ground-truth. Experimental results have pointed out that the use of new proposed score was able to improve final segmentation results. |
| Databáze: | OpenAIRE |
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