Saliency Mapping Enhanced by Structure Tensor

Computational Intelligence and Neuroscience
Zhiyong HeLining Sun

Abstract

We propose a novel efficient algorithm for computing visual saliency, which is based on the computation architecture of Itti model. As one of well-known bottom-up visual saliency models, Itti method evaluates three low-level features, color, intensity, and orientation, and then generates multiscale activation maps. Finally, a saliency map is aggregated with multiscale fusion. In our method, the orientation feature is replaced by edge and corner features extracted by a linear structure tensor. Following it, these features are used to generate contour activation map, and then all activation maps are directly combined into a saliency map. Compared to Itti method, our method is more computationally efficient because structure tensor is more computationally efficient than Gabor filter that is used to compute the orientation feature and our aggregation is a direct method instead of the multiscale operator. Experiments on Bruce's dataset show that our method is a strong contender for the state of the art.

References

Dec 29, 2004·Vision Research·Benjamin W TatlerIain D Gilchrist
Jan 17, 2009·Journal of Vision·Lingyun ZhangGarrison W Cottrell
Sep 18, 2009·Journal of Vision·Neil D B Bruce, John K Tsotsos
Jan 5, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Tie LiuHeung-Yeung Shum
May 10, 2011·Vision Research·Marisa Carrasco
Jul 27, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence· Xiaodi HouChristof Koch
Dec 28, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Stas GofermanAyellet Tal
Apr 11, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ali Borji, Laurent Itti

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Citations

May 28, 2019·Journal of Applied Clinical Medical Physics·Tiejun YangLu Tang

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Methods Mentioned

BETA
feature extraction
features extraction

Software Mentioned

Itti
MATLAB

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