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2018 | OriginalPaper | Buchkapitel

Salient Object Detection Based on the Fusion of Foreground Coarse Extraction and Background Prior

verfasst von : Lingkang Gu, Zhigeng Pan

Erschienen in: Transactions on Edutainment XIV

Verlag: Springer Berlin Heidelberg

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Abstract

In order to obtain more refined salient object detection results, firstly, the coarse salient regions are extracted from the bottom-up, the coarse saliency map contains local map, frequency prior map and global color distribution map, which are more in accord with the rules of biological psychology. Then, an algorithm is proposed to measure the background prior quality by using three indexes, namely, salient expectation, local contrast and global contrast. Finally, the weighted algorithm is designed according to the prior quality to improve the saliency, so that the saliency prior and the saliency detection results are more accurate. Compared with 9 state-of-the-art algorithms on the 2 benchmark datasets of ECSSD and MSRA 10k, the proposed algorithm highlights salient regions, reduces noise, and is more in line with human visual perception, and reflects the excellence.

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Titel: Salient Object Detection Based on the Fusion of Foreground Coarse Extraction and Background Prior
verfasst von: Lingkang Gu
Zhigeng Pan
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Edutainment XIV
Print ISBN: 978-3-662-56688-6

Electronic ISBN: 978-3-662-56689-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-662-56689-3_9

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