Learning Active Basis Models by EM-Type Algorithms

TL;DR

This paper reviews the use of EM algorithms for learning image templates called active basis models, which can handle deformations and unknown object poses in images, enabling unsupervised learning of object categories.

Contribution

It introduces an EM-based framework for learning active basis models that account for object deformations and unknown poses, bridging supervised and unsupervised learning.

Findings

Effective learning of object templates with pose variations

EM algorithm alternates between recognition and template updating

Experimental results demonstrate the method's robustness

Abstract

EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is not fully supervised. We represent an image template by an active basis model, which is a linear composition of a selected set of localized, elongated and oriented wavelet elements that are allowed to slightly perturb their locations and orientations to account for the deformations of object shapes. The model can be easily learned when the objects in the training images are of the same pose, and appear at the same location and scale. This is often called supervised learning. In the situation where the objects may appear at different unknown locations, orientations and scales in the training images, we have to incorporate the unknown locations, orientations and scales as latent variables into the image generation process, and learn the template by EM-type algorithms. The E-step imputes the unknown locations, orientations and scales based on the currently learned template. This step can be considered self-supervision, which involves using the current template to recognize the objects in the training images. The M-step then relearns the template based on the imputed locations, orientations and scales, and this is essentially the same as supervised learning. So the EM learning process iterates between recognition and supervised learning. We illustrate this scheme by several experiments.