Visual Selective Model And Its Application In Image Classification

Visual attention is one of the most important mechanisms of the human visual system. It enables us to locate regions of interest in a complex scene, in order to act effectively in our environment. The visual selective mechanism mainly includes two steps: bottom-up mechanism which is rapid and data-driven; top-down mechanism which is slow and task-driven. More often than not, there are several salient regions in the image while the other regions can be viewed as the background which are useless to the understanding of the current image and may bring complexity to the image processing. Through the human selective mechanism, we can locate the salient regions and do not have to process the information of the whole scene.The existing visual selective model mainly focuses on the bottom-up mechanism. However, these models are not very efficient to locate salient points in some situations. Meanwhile an image can not be fully described only through a visual selective model because a salient feature can become less salient in certain situations. Humans may become attracted by features which are in minority. This paper proposes a way of combining visual selective model with global rarity to group together images. The attention features extracted through this method focus on the description of objects which may attract human attention. Experimental results show that the proposed approach works well for image classification and the average accuracy rate can reach 97.74%.