A Hierarchical GIST Model Embedding Multiple Biological Feasibilities for Scene Classification

We propose a hierarchical GIST model embedding multiple biological feasibilities for scene classification. In the perceptual layer, spatial layout of Gabor features are extracted in a bio-vision guided way: introducing diagnostic color information, tuning the orientations and scales of Gabor filters, as well as the spacial pooling size to a biological feasible value. In the conceptual layer, for the first time, we attempt to build a computational model for the biological conceptual GIST by kernel PCA based prototype representation, which is specific task orientated as biological GIST, and also in accordance with the unsupervised learning assumption in the primary visual cortex and prototype similarity based categorization in human cognition. Using around 200 dimensions, our model is shown to outperform existing GIST models, and to achieve state-of-the-art performances on four scene datasets.