1

Learning to Detect Natural Image Boundaries

David Martin, Charless Fowlkes, Jitendra Malik

Computer Science Division

University of California at Berkeley

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

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

DataflowImage

Optimized CuesPb

Brightness

Color

Texture

Benchmark

Human Segmentations

Cue Combination

Model

5

Boundary Detection Output

Canny 2MM Us HumanImage

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.