Biologically plausible context recognition algorithms

In this paper, four new approaches of global context recognition algorithms (gist) are introduced. They are able to automatically distinguish context differences like buildings, coast, home (indoor), mountain or streets. All proposed models are biologically plausible and are able to deal with both color and gray-level images. They use Gabor or Log-Gabor filters to extract features that better mimic human visual perception. Those features are then classified using a Mahalanobis space (when a subset of features is extracted) or in a high-dimensional Gaussian space (when all features are taken into account) with Support Vector Machines (SVM). The proposed models are compared to a standard state of the art gist model to proof their efficiency.