一种新的自底向上的注意信息提取算法

Inspired by research of visual attention in psychology, a novel algorithm for extracting bottom-up attention information is proposed in the paper. Bottom-up attention information is composed by saliency of certain regions correspond to each point in image, and scale of the region varies with complexity of local features adaptively. New saliency metric is defined as a product of three terms: local complexity, statistical dissimilarity and early visual features. Salient regions are salient both in feature space and over scale. The extracted bottom-up attention information is invariant to image scale, rotation and translation, and is shown to be robust to noise. An attention model is developed based on this algorithm. Experiments results with natural images demonstrate its effectiveness.