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Improving Image Caption Performance with Linguistic Context Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Salient Object Detection via Graph-Based Flexible Manifold Ranking

Authors : Ying Yang, Bo Jiang, Yun Xiao, Jin Tang

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

The task of saliency detection is to segment salient objects in natural scenes. Simple and effective saliency detection model has always been a challenging problem. We explore a graph-based flexible manifold ranking approach for single image saliency detection. An input image is represented as an undirected graph. Feature vectors are extracted covering regional color and texture. An optimal function is used to infer the labels based on linear classification projection and manifold ranking in our work. The optimal function further ensures the reliability of the prediction results. Extensive experiments on four benchmark datasets show that our method is better than the other eight classic methods. So the proposed method is a competitive method.

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previous chapter Forensic Detection Based on Color Label and Oriented Texture Feature
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Metadata
Title
Salient Object Detection via Graph-Based Flexible Manifold Ranking
Authors
Ying Yang
Bo Jiang
Yun Xiao
Jin Tang
Copyright Year
2020
Book
Advances in Brain Inspired Cognitive Systems

Print ISBN: 978-3-030-39430-1

Electronic ISBN: 978-3-030-39431-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39431-8_38

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