Saliency Mapping Enhanced by Structure Tensor.

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.

journal_name

Comput Intell Neurosci

authors

He Z,Chen X,Sun L

doi

10.1155/2015/875735

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

875735

eissn

1687-5265

issn

1687-5273

journal_volume

2015

pub_type

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