Segmentation of RGB-D Indoor Scenes by Stacking Random Forests and Conditional Random Fields
Studenteropgave: Speciale (inkl. HD afgangsprojekt)
- Mikkel Thøgersen
4. semester, Vision, Grafik og Interaktive Systemer, Kandidat (Kandidatuddannelse)
This report proposes a new model using the Multi-class Multi-scale Stacked Sequential Learning framework as the solution to the problem of indoor semantic segmentation. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels and are used in a Conditional Random Field to predict superpixel labels. Furthermore, a Random Forest classifier using random offset features is also used as an input to the Conditional Random Field, acting as an initial prediction.
As a stacked classifier, another Random Forest is used which acts on a spatial multi-scale decomposition of the confidence map to correct the erroneous labels assigned by the previous classifier.
The model is tested and trained on the popular NYU-v2 dataset. The approach shows that simple features with the power of the MMSSL framework can achieve better performance than similar methods.
As a stacked classifier, another Random Forest is used which acts on a spatial multi-scale decomposition of the confidence map to correct the erroneous labels assigned by the previous classifier.
The model is tested and trained on the popular NYU-v2 dataset. The approach shows that simple features with the power of the MMSSL framework can achieve better performance than similar methods.
Sprog | Engelsk |
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Udgivelsesdato | 3 jun. 2015 |
Antal sider | 89 |
Emneord | RGBD, Segmentering, semantik, dybde, Conditional Random Field, Random Forest, Stacked Learning |
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