TY - JOUR AU - Mikkel Thogersen AU - Sergio Escalera AU - Jordi Gonzalez AU - Thomas B. Moeslund PY - 2016// TI - Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields T2 - PRL JO - Pattern Recognition Letters SP - 208–215 VL - 80 N2 - This paper proposes a technique for RGB-D scene segmentation using Multi-classMulti-scale Stacked Sequential Learning (MMSSL) paradigm. 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 (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.The approach shows that simple multi-modal features with the power of the MMSSLparadigm can achieve better performance than state of the art results on the same dataset. UR - https://doi.org/10.1016/j.patrec.2016.06.024 N1 - HuPBA; ISE;MILAB; 600.098; 600.119 ID - Mikkel Thogersen2016 ER -