一种图斑特征引导的感知分组视觉注意模型

 结合自顶向下和自底向上的信息处理策略,提出了一种新的视觉注意模型,该模型利用图斑特征信息引导感知分组过程,使注意力关注于任务相关区域。通过引入多尺度图斑,关联图斑和底层特征,新模型利用图斑特征建立先验信息的知识表达形式。对于给定新的场景,新模型能够使用先验信息,提高和目标对象相关特征的显著性。通过视觉预注意阶段计算得到的中间数据,提取图斑特征向量作为引导,不断迭代积累对象,合并区域表征对象,由表征简单对象开始,进而表征复杂对象,迅速有效地引导视觉注意力关注任务相关区域。实验比较了新模型、显著区域提取模型及波谱残留模型,证明了所提模型的优越性。

A Novel Visual Attention Model Based on Blob-guided Perceptual Grouping

By combining bottom-up and top-down information, a novel visual attention model based on blob-guided perceptual grouping is proposed in this article. The model can build knowledge representations for prior information by means of blob features through introducing multi-level blobs and connecting blob properties and low-level features. For any new given scene, the model can use the prior knowledge to render the object features more salient by enhancing those characteristic features of the object, and then it groups regions together recursively to form objects, guided by blob feature vectors extracted from the intermediate data at the pre-attention stage. Selective visual attention in the model can be effectively directed to task-relevant regions. A comparison of the model with other attention models which can direct attention to salient proto-objects proves its superiority.