Bayesian Saliency via Low and Mid Level Cues

Visual saliency detection is a challenging problem in computer vision, but one of great importance and numerous applications. In this paper, we propose a novel model for bottom-up saliency within the Bayesian framework by exploiting low and mid level cues. In contrast to most existing methods that o...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 22; H. 5; S. 1689 - 1698
Hauptverfasser:	Xie, Yulin, Lu, Huchuan, Yang, Ming-Hsuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.05.2013 Institute of Electrical and Electronics Engineers
Schlagworte:	Applied sciences Artificial intelligence Bayesian methods Clustering algorithms Computational modeling Computer science; control theory; systems Exact sciences and technology Image color analysis Image processing Information, signal and communications theory Laplace equations Laplacian sparse subspace clustering Pattern recognition. Digital image processing. Computational geometry saliency map Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Sparse matrices Telecommunications and information theory visual saliency Visualization Bayes estimation Computer vision Automatic classification State of the art Subspace method saliency map Laplacian sparse subspace clustering Algorithm Signal classification Laplacian Convex hull Bayes methods Visual saliency
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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