Structured Forests for Fast Edge Detection

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Presentation transcript:

Structured Forests for Fast Edge Detection Piotr Dollár and Larry Zitnick

Let the data speak. what defines an edge? Brightness Color Texture Parallelism Continuity Symmetry … Let the data speak.

1. Accuracy 2. Speed

I. data driven edge detection

edge detection as classification { 0, 1 } Hard! positives Supervised Learning of Edges and Object Boundaries CVPR 2006, Piotr Dollár, Zhuowen Tu, Serge Belongie

edge have structure

sketch tokens Sketch Tokens, CVPR 2013. Joseph Lim, C. Zitnick, and P. Dollár

random forests

upgrading the output space dimensionality 2 { 0, 1 } { … } 151 2256

II. structured edge learning

structured forests Structured Class-Labels in Random Forests for Semantic Image Labelling, ICCV 2011, P. Kontschieder, S. Rota Bulò, H. Bischof, M. Pelillo

tree training

node training low entropy split  high entropy split 

how to train? ? ? good split  bad split 

minimize entropy cluster ? ?

III. structured edge detection

structured forests

sliding window detector

sliding window detector Comparison to storing only single pixel prediction Noisy, no coherent structure Would need far more trees! This is the secret to our speed / accuracy. pixel output  structured output 

multiscale detection

multiscale detection + + ½ x 1 x 2 x

IV. results

SS=single-scale MS=multi-scale T=# trees SS T=1 ODS = 0.72 60Hz SS T=4 ODS = 0.73 30Hz MS T=4 ODS = 0.74 6Hz gPb ODS=.73 FPS ≈ 1/240 Hz SS=single-scale MS=multi-scale T=# trees gPb ODS=.73

thanks! source code available online