# Weakly supervised learning of hierarhical active basis

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Contents

- Download
- Training examples
- Learned partial templates
- Shape matching by hierarchical active basis

Code and data: (ZIP). | Change Log
Run StartFromHere.m in matlab to learn the hierarchical active basis template. Run GenerateHtml.m to generate this webpage.

## Training examples

We learn a 2-level hierarchical active basis template from images with articulated objects with unknown scale, rotation and translation. The object template size is 240 * 240 pixels; the part template size is 60 * 60 pixels. Each part may translate and rotate locally relative to the object center. And each edge element can translate and rotate locally relative to the part center. We use EM-type algorithm to obtain the part and object templates as well as the hierarchical deformation on each image. Number of EM iterations = 5. Each part is an active basis model of Gabor elements with local shift <= 3 pixels, and orientation shift <= 1 * PI/16. In total, there are 130 Gabor elements in the object template.

### Learning from example 1

The template is initialized from the following example image.

## Learned object template

## Shape matching by Hierarchical Active Basis

A hierarchical active basis (HAB) template is composed of a small number of partial templates which can translate and rotate locally relative to other partial templates.The following pairs of images show the matched HAB templates on example images.