2014/12/13 by Sam Hallman, Hallman, Sam, Charless C. Fowlkes +1 · 2 citations
Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Landslides and related hazards #Neural Networks and Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.1412.4181
updated to include contents of CVPR version + new figure showing example segmentation results
openalex publication_date 2014/12/13 · arxiv created 2015/06/28 · arxiv updated 2015/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a simple, efficient model for learning boundary detection based on a random forest classifier. Our approach combines (1) efficient clustering of training examples based on simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of individual tree output probabilities prior to multiscale combination. The resulting model outperforms published results on the challenging BSDS500 boundary detection benchmark. Further, on large datasets our model requires substantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.