A random center surround bottom up visual attention model useful for salient region detection

Authors:
Tadmeri Narayan Vikram;Marko Tscherepanow;Britta Wrede
Affiliations:
Applied Informatics Group, Bielefeld University, Bielefeld, Germany;Applied Informatics Group, Bielefeld University, Bielefeld, Germany;Applied Informatics Group, Bielefeld University, Bielefeld, Germany
Venue:
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Year:
2011

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A visual saliency map based on random sub-window means

IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A saliency map based on sampling an image into random rectangular regions of interest

Pattern Recognition

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Abstract

In this article, we propose a bottom-up saliency model which works on capturing the contrast between random pixels in an image. The model is explained on the basis of the stimulus bias between two given stimuli (pixel intensity values) in an image and has a minimal set of tunable parameters. The methodology does not require any training bases or priors. We followed an established experimental setting and obtained state-of-the-art-results for salient region detection on the MSR dataset. Further experiments demonstrate that our method is robust to noise and has, in comparison to six other state-of-the-art models, a consistent performance in terms of recall, precision and F-measure.