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Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

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PROJECT TITLE :

Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

ABSTRACT:

We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterised by a collection of binary pictures, that are generated by randomly thresholding the image's feature maps in a very whitened feature area. Primarily based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we tend to draw a association between BMS and the Minimum Barrier Distance to produce insight into why and the way BMS will properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, potency and superior performance compared with ten state-of-the-art methods on seven eye tracking benchmark datasets.


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Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach - 4.9 out of 5 based on 18 votes

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