A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes
AbstractHumans 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.
CitationPLoS One. 2011 May 25; 6(5):e20002
- Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.
- Authors: Xu J, Yang Z, Tsien JZ
- Issue date: 2010 Dec 29
- The Neural Dynamics of Attentional Selection in Natural Scenes.
- Authors: Kaiser D, Oosterhof NN, Peelen MV
- Issue date: 2016 Oct 12
- Neural mechanisms of rapid natural scene categorization in human visual cortex.
- Authors: Peelen MV, Fei-Fei L, Kastner S
- Issue date: 2009 Jul 2
- How can selection of biologically inspired features improve the performance of a robust object recognition model?
- Authors: Ghodrati M, Khaligh-Razavi SM, Ebrahimpour R, Rajaei K, Pooyan M
- Issue date: 2012
- Recognition of natural scenes from global properties: seeing the forest without representing the trees.
- Authors: Greene MR, Oliva A
- Issue date: 2009 Mar