The Spectral Residual approach for Static Saliency

Implementation of SpectralResidual for Static Saliency.

Saliency API

Many computer vision applications may benefit from understanding where humans focus given a scene. Other than cognitively understanding the way human perceive images and scenes, finding salient regions and objects in the images helps various tasks such as speeding up object detection, object recognition, object tracking and content-aware image editing.

About the saliency, there is a rich literature but the development is very fragmented. The principal purpose of this API is to give a unique interface, a unique framework for use and plug sever saliency algorithms, also with very different nature and methodology, but they share the same purpose, organizing algorithms into three main categories:

• Static Saliency
• Motion Saliency
• Objectness

Saliency UML diagram: <<http://docs.opencv.org/3.1.0/saliency.png>>

To see how API works, try tracker demo: computeSaliency_demo.m.

Static Saliency algorithms

Algorithms belonging to this category, exploit different image features that allow to detect salient objects in a non dynamic scenarios.

Presently, the Spectral Residual approach [SR] has been implemented.

Spectral Residual

Starting from the principle of natural image statistics, this method simulate the behavior of pre-attentive visual search. The algorithm analyze the log spectrum of each image and obtain the spectral residual. Then transform the spectral residual to spatial domain to obtain the saliency map, which suggests the positions of proto-objects.

References

[SR]:

Xiaodi Hou and Liqing Zhang. "Saliency detection: A spectral residual approach". In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pages 1-8. IEEE, 2007.