A model for image categorisation based on a biological visual mechanism

For integrating a visual attention mechanism and object recognition in the visual cortex we propose a novel biologically-motivated computational model for image categorisation. We first extract the focus of attention using an image-driven, bottom-up attention model and then adjust it according to the principles of whole effect and centre preference. After that, we obtain the region of interest, depending on the characteristics of object spatial proximity and object similarity. Based on this we compute a set of position- and scale-invariant C2 features and finally pool them into the standard classifier to achieve image categorisation. We test our model on an image database used in SIMPLIcity. The results suggest that our model can not only classify images effectively under various complex "clutters" but also that it needs only a few training samples.