Visual dictionaries as intermediate features in the human brain.

ABSTRACT: The human visual system is assumed to transform low level visual features to object and scene representations via features of intermediate complexity. How the brain computationally represents intermediate features is still unclear. To further elucidate this, we compared the biologically plausible HMAX model and Bag of Words (BoW) model from computer vision. Both these computational models use visual dictionaries, candidate features of intermediate complexity, to represent visual scenes, and the models have been proven effective in automatic object and scene recognition. These models however differ in the computation of visual dictionaries and pooling techniques. We investigated where in the brain and to what extent human fMRI responses to short video can be accounted for by multiple hierar

SUBMITTER: Ramakrishnan K 

PROVIDER: S-EPMC4295527 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature