Visual Attention Based Image Classification
一种基于视觉注意模型的图像分类方法

Visual attention is one of the most important mechanisms of the human visual system (HVS). Recent research has demonstrated that a bottom-up visual selective model can be applied to problems such as target recognition. Nevertheless,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. Experimental results show that the proposed approach works well for image classification and the average accuracy rate can reach 97.74%.