视觉选择性注意机制是人类视觉系统的重要组成部分。近年来的研究表明，自下而上的视觉选择性注意模型在物体识别等方面得到了良好的应用。但是，视觉选择性注意模型在描述图像内容时存在着明显的不足，一个显著的特征在某些情况下可能不会得到注意，人眼更可能会注意到一幅图像里比较稀少的特征。针对上述情况，提出了一种基于视觉选择性注意模型和全局稀少性相结合的视觉注意模型进行图像分类。实验结果表明，该方法在多类物体分类中达到9774%的总准确率，取到了非常好的效果。

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%.