TR#326: Probabilistic Visual Learning for Object Detection

Baback Moghaddam and Alex Pentland

Appears in:
Proceedings of the 5th International Conference on Computer Vision,
Cambridge, MA, June 20-23, 1995.

We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for a unimodal distribution) and a multivariate Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition. This learning technique is tested in experiments with modeling and subsequent detection of human faces and non-rigid objects such as hands.

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