A Novel Bayes' Theorem-Based Saliency Detection Model

Xin HE  Huiyun JING  Qi HAN  Xiamu NIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.12   pp.2545-2548
Publication Date: 2011/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.2545
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
visual attention,  saliency map,  Bayes' theorem,  kernel density estimation,  

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We propose a novel saliency detection model based on Bayes' theorem. The model integrates the two parts of Bayes' equation to measure saliency, each part of which was considered separately in the previous models. The proposed model measures saliency by computing local kernel density estimation of features in the center-surround region and global kernel density estimation of features at each pixel across the whole image. Under the proposed model, a saliency detection method is presented that extracts DCT (Discrete Cosine Transform) magnitude of local region around each pixel as the feature. Experiments show that the proposed model not only performs competitively on psychological patterns and better than the current state-of-the-art models on human visual fixation data, but also is robust against signal uncertainty.