Main Purpose:
* The main goal of the proposed method is to exploit a global perception
mechanism, known as figure-ground segregation and Boolean Map Theory of
visual attention to compute saliency map.

Drawbacks of previous works:
* Most of the previous works do not exploit the topological structures of an image to
saliency calculation. Thus this paper aims to exploit the topological structure of a
scene in saliency calculation.

Main Idea:
* Relying on Boolean Map Theory of visual attention, an observer’s momentary
conscious awareness of a scene can be represented by a Boolean map. Given an
input image, a set of Boolean maps can be generated by randomly thresholding its
feature channels, e.g. color channel. When the set of Boolean maps is generated,
some concepts from figure-ground segregation, introduced by Gestalt
psychological studies, can be utilized to create a saliency map. As Gestalt
psychological studies suggest, figures are more likely to be attended than to
background elements. But how the figures are characterized? There is some factors
that are likely to influence figure-ground segregation such as size, surroundedness,
symmetry and etc. The proposed method uses the surroundedness factor to form a
saliency map. This factor implies that figures tends to be surrounded, that is,
they’re likely to have a closed outer contour. Therefore, this factor can be
evaluated over Boolean maps and each region which has the closed outer contour,
not connected to the image borders, gets higher attention value. So each Boolean
map result in an attention map. Then, attentions maps are summed up lineally to
form a saliency map.
In brief, the contribution of the proposed method is twofold: 1) It uses Boolean
maps to characterizes an image. 2) It exploits the figure-ground segregation
concepts to form a saliency map.

Implementation Details:
* Given an input image, a set of Boolean maps are generated by thresholding the
color channels (Lab) with uniformly selected thresholds from 0 to 255. Then, each
Boolean map is evaluated by surroundedness. Those regions which are surrounded
(closed outer contour) get value 1 and others 0. These maps are called attention
maps. After some normalization steps, the attention maps (per Boolean map) are
summed up linearly to form a mean attention map. Again, some normalization and
post-processing steps are applied and the final saliency map is obtained.

* Conclusion
In this work, a novel Boolean Map based Saliency model is proposed to leverage
the surroundedness cue that helps in figure-ground segregation. The model
borrows the concept of Boolean map from the Boolean Map Theory of visual
attention and characterizes an image by a set of Boolean maps. This representation
leads to an efficient algorithm for saliency detection. BMS is the only model that
consistently achieves state-of-the-art performance on five benchmark eye tracking
datasets, and it is also shown to be useful in salient object detection.