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 Neuroscijournal_title
Computational intelligence and neuroscienceauthors
He Z,Chen X,Sun Ldoi
10.1155/2015/875735subject
Has Abstractpub_date
2015-01-01 00:00:00pages
875735eissn
1687-5265issn
1687-5273journal_volume
2015pub_type
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