1. 1 Learning to Detect Natural Image Boundaries David Martin, Charless Fowlkes, Jitendra Malik Computer Science Division University of California at Berkeley

2. 2 Goal: A Computational Model of Vision 1.Image Segmentation –Parsing, from pixels to regions Rocks Ice Penguin Shadow Wing 2.Object Recognition –Grouping and labeling of regions

3. 3 An Empirical Approach Use 1000 images, each segmented by 10 human subjects in order to establish ground truth Evaluate hundreds of algorithmic design choices based on performance on test data set. Calibrate parameters to best match human data.

4. 4 Dataflow Image Optimized Cues PbPb Brightness Color Texture Benchmark Human Segmentations Cue Combination Model

5. 5 Boundary Detection Output Canny2MMUsHumanImage

6. 6 Summary Around 20 processor years worth of experiments (10 – 20 experiments a day, each run on set of 300 images) Final product is a boundary detector which outperforms existing methods and matches human performance for the local boundary detection task.