A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes

Abstract

Humans can categorize objects in complex natural scenes within 100â 150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., â ¼6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization.