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2015 | OriginalPaper | Buchkapitel

Robust Salient Object Detection and Segmentation

verfasst von : Hong Li, Wen Wu, Enhua Wu

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

Background prior has been widely used in many salient object detection models with promising results. These methods assume that the image boundary is all background. Then, color feature based methods are used to extract the salient object. However, such assumption may be inaccurate when the salient object is partially cropped by the image boundary. Besides, using only color feature is also insufficient. We present a novel salient object detection model based on background selection and multi-features. Firstly, we present a simple but effective method to pick out more reliable background seeds. Secondly, we utilize multi-features enhanced graph-based manifold ranking to get the saliency maps. Finally, we also present the salient object segmentation via computed saliency map. Qualitative and quantitative evaluation results on three widely used data sets demonstrate significant appeal and advantages of our technique compared with many state-of-the art models.

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Vorheriges Kapitel Robust Face Recognition with Locality-Sensitive Sparsity and Group Sparsity Constraints

Nächstes Kapitel Rough Lane Marking Locating Based on Adaboost

For different channels in LAB and RGB color spaces, we perform the calculation separately and add the results together to get the corresponding feature descriptor.

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Titel: Robust Salient Object Detection and Segmentation
verfasst von: Hong Li
Wen Wu
Enhua Wu
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-21968-4

Electronic ISBN: 978-3-319-21969-1

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-21969-1_24

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