一个基于布尔图的显著性检测模型这个模型利用了全局拓扑测度，这一拓扑测度已经被证明了有助于感知的图形-背景分割。如格式塔心理学研究表明，如下因素很可能会影响图形-背景分割：大小，surroundedness，convexity, symmtry。本文研究surroundedness cue在显著性检测中的应用。 surroundedness的本质是图形和背景之间的enclosure topological relationship，能够被很好地定义，并且对不同的变化具有不变性（不懂）。 为了衡量surroundedness，BMS用二值图像集合表示图像。在BMS中，一个注意图(attention map)通过二值图处理技术激活给定的布尔图中的具有closed outer contours的区域来计算获得。显著性则根据期望注意图获得，也就是平均注意图。这个平均主义通过后续的处理可以用注视点的预测或是显著对象的检测。-A novel Boolean Map based Saliency (BMS) model is

proposed. An image is characterized by a set of binary

images, which are generated by randomly thresholding the

image’s color channels. Based on a Gestalt principle of

figure-ground segregation, BMS computes saliency maps

by analyzing the topological structure of Boolean maps.

BMS is simple to implement and efficient to run. Despite

its simplicity, BMS consistently achieves state-of-the-art

performance compared with ten leading methods on five eye

tracking datasets. Furthermore, BMS is also shown to be

advantageous in salient object detection.